arXiv:2605.17573v1 [cs.CV] 17 May 2026
Deepfake Detection in Social Media: A Temporal Artifact Analysis Using 3D Convolutional Neural Networks Mohammadreza Rashidi
Raja Hashim Ali
Sami Ur Rahman
Department of Computer Science AI and Media Analysis Lab Berlin, Germany [email protected]
Department of Computer Science AI and Media Analysis Lab Berlin, Germany [email protected]
Department of Computer Science AI and Media Analysis Lab Berlin, Germany [email protected]
Abstract—Synthetic facial videos have proliferated across social media faster than platform moderation can respond, raising the cost of disinformation and identity-based attacks. Framelevel deepfake detectors degrade sharply as generator quality increases; high-quality 128×128 GAN output cuts spatial-only accuracy by five percentage points while leaving temporal inconsistencies largely intact. We address this gap with a 3D Convolutional Neural Network detector based on R3D-18, trained with a composite loss that combines binary cross-entropy with a temporal-consistency regularizer. The model processes 16frame clips from the DeepfakeTIMIT dataset and is initialized from Kinetics-400 action-recognition weights. We report 92.8% accuracy on intra-dataset evaluation at 128×128 resolution; cross-dataset transfer to FaceForensics++ without fine-tuning reaches 76.4%, rising after minimal fine-tuning. Ablation studies show that transfer learning contributes 7.2 percentage points and face tracking adds 3.5 points, while temporal consistency regularization provides additional gains on high-quality fakes. The results establish that temporal artifacts generalize more broadly than spatial ones, providing a detection signal that survives social-media re-encoding. Index Terms—Deepfake detection, 3D CNN, temporal artifacts, video analysis, synthetic media, face manipulation
I. I NTRODUCTION Synthetic facial videos have spread widely across social media since GAN-based generation tools became accessible to non-expert users [1]. A convincing deepfake can be produced in minutes; once uploaded, platform recommendation algorithms accelerate its reach before any moderation can act. Threats range from targeted harassment and identity fraud to large-scale disinformation campaigns, and they all share a technical vulnerability: current generation engines that fool the human eye still leave traces in the time domain that span multiple frames [2], [3]. Detection research has addressed the spatial dimension extensively. Methods based on XceptionNet and related architectures identify artifacts within individual frames and achieve over 89% accuracy under controlled conditions. Yet framelevel detectors degrade sharply as generator quality rises; highquality 128×128 output cuts spatial-only accuracy by five perCode and pretrained weights will be made available upon acceptance.
centage points while leaving temporal inconsistencies largely intact [4]. Social media compounds the difficulty: platform re-encoding discards fine spatial detail, yet temporal patterns such as irregular blinking, stuttering micro-expressions, and misaligned head-pose transitions survive compression and remain detectable [5]. Three-dimensional convolutional networks process clips rather than frames, making them a natural fit for this setting. R3D-18, pre-trained on Kinetics-400 action recognition, already encodes priors about natural human motion; finetuning it for facial manipulation allows the model to detect temporal artifacts that spatial networks miss [6]. Cross-dataset generalization remains an open problem: models trained on one benchmark transfer poorly to another when they overfit to generator-specific spatial textures instead of generator-agnostic temporal signals [7]. This paper investigates how temporal artifact analysis with a 3D CNN can close the generalization gap. We train on DeepfakeTIMIT and evaluate on FaceForensics++ with and without fine-tuning, measuring how much temporal modeling accounts for cross-dataset transfer. We also formalize the training objective with an explicit temporal-consistency regularizer and conduct ablation studies that isolate each design choice. Our contributions are: A temporal deepfake detector based on R3D-18 with a composite loss combining binary cross-entropy with a frame-wise feature-consistency penalty, improving accuracy on high-quality deepfakes by 4.5 percentage points over the frame-level XceptionNet baseline. • Quantitative identification of eye-blinking sequences and micro-expression transitions as the most discriminative temporal artifacts, providing actionable targets for both detection and generation research. • Cross-dataset experiments showing 76.4% zero-shot accuracy on FaceForensics++ and 92.8% accuracy on highquality intra-dataset evaluation (128×128 resolution), confirming that temporal signals generalize more broadly than spatial ones. •
•
An ablation study decomposing the contributions of transfer learning, face tracking, sequence length, and the temporal-consistency term to overall detection performance. II. L ITERATURE R EVIEW
The deepfake detection field spans four decades of media forensics yet has been transformed in the past two years by the combination of large pretrained vision models and newly available benchmarks. This section organizes prior work by architectural strategy rather than by publication year. A. Spatial CNN Detectors Early CNN-based detectors operate on individual frames and aggregate predictions across time. XceptionNet, adapted by Rossler et al. [8] from an ImageNet classifier, remains a strong baseline at 89.7% accuracy on DeepfakeTIMIT; its depth-wise separable convolutions efficiently identify intraframe compression and blending artifacts. Ahmad et al. [9] introduced FAME, a lightweight spatio-temporal network that attributes a detected fake to its source generation model, providing forensically actionable intelligence that goes beyond binary real/fake classification. Lanzino et al. [10] developed a binary neural network detector for real-time deepfake detection, demonstrating that heavily compressed network architectures retain competitive accuracy while achieving the throughput required for online content moderation. Srivasthav et al. [7] employed adaptive meta-learning in a multi-agent framework to address data drift and cross-domain generalization, showing that models relying on spatial artifacts alone degrade significantly on unseen manipulation types and motivating the temporal approaches discussed below.
C. Transformer-Based Approaches Transformer architectures have become the predominant paradigm for high-accuracy detection, with self-attention enabling fine-grained comparison of feature patches across both space and time. Zhang et al. [3] proposed learning natural consistency representations for face forgery video detection, using multi-head temporal attention to capture both within-frame spatial patch relationships and cross-frame motion dependencies. Nguyen et al. [2] introduced vulnerability-aware spatiotemporal learning that identifies and exploits regions where deepfake generators are most likely to produce detectable artifacts, achieving strong generalization across unseen manipulation methods. Nguyen et al. [13] presented FakeFormer, an efficient vulnerability-driven transformer that maintains generalisable detection performance while reducing inference cost, making transformer-based detectors more suitable for real-time content moderation. Yan et al. [5] developed a plug-and-play generalization strategy combining video-level blending with spatiotemporal adapter tuning, enabling existing transformer detectors to transfer more effectively across domains without full retraining. Further architectural refinements include STKD-VViT [14], which applies knowledge distillation to a video vision transformer to reduce parameter count while preserving multimodal detection accuracy; SFormer [15], an end-to-end Swin-Transformer-based spatio-temporal pipeline; and CoDeiT [16], a contrastive data-efficient transformer that learns discriminative representations from fewer labeled examples. Chen et al. [17] combined spatio-temporal consistency analysis with attention-based feature selection, while Yan et al. [18] proposed DF40, a large-scale benchmark covering forty manipulation methods designed to stress-test detectors against next-generation synthesis techniques. D. Frequency-Domain Analysis
B. Temporal and 3D Convolutional Models Moving beyond per-frame analysis, 3D CNNs jointly convolve over spatial and temporal dimensions, capturing artifact patterns that require multiple consecutive frames to manifest. Chu et al. [4] showed that reducing spatial dependency in 3D CNN detectors improves cross-dataset generalization, a critical property for deployment on the heterogeneous video streams found on social media platforms. Cavia et al. [11] addressed the gap between benchmark accuracy and realworld effectiveness, introducing a patch-level detector and the WildRF evaluation dataset to measure performance on in-thewild deepfakes rather than controlled benchmarks. Gandhi et al. [6] proposed a multimodal detection framework that jointly processes visual and supplementary cues, showing that combining complementary feature streams produces more robust detection than any single stream alone. Fang et al. [12] introduced UniForensics, a unified face forgery detection framework that uses dynamic video self-blending to generate training samples with diverse spatio-temporal forgery traces, enabling self-supervised learning of generalizable temporal representations without manual forgery labeling.
Frequency-domain methods exploit the observation that GAN and diffusion generators leave distinctive spectral footprints that survive mild spatial post-processing but can be disrupted by aggressive compression. Tan et al. [19] proposed a frequency-aware detector that learns which frequency bands carry the most discriminative information for each manipulation type, improving generalizability to unseen generators by operating in frequency space rather than pixel space. Chen et al. [20] addressed compressed video deepfake detection using 3D spatiotemporal trajectories, showing that trajectorybased frequency representations remain informative even after aggressive re-encoding by social media platforms. Baru et al. [21] introduced Wavelet-CLIP, a generalizable framework that combines wavelet-transform frequency features with CLIP-pretrained ViT representations, demonstrating strong cross-dataset generalization including robustness to diffusionmodel-generated fakes. Luo et al. [22] extended frequency masking with explicit spatial interaction terms, allowing the model to correlate spectral anomalies with their spatial origin and improving detection on both compressed and high-quality video.
E. Multimodal Fusion When audio is available, audio-visual consistency provides detection signals that are orthogonal to both spatial and temporal visual cues. Oorloff et al. [23] introduced AVFF, an audio-visual feature fusion network that fuses per-modality representations via cross-attention; validated at CVPR 2024, AVFF sets a strong baseline for audio-visual deepfake detection on the FakeAVCeleb benchmark. Katamneni and Rattani [24] proposed contextual cross-modal attention for joint audio-visual detection and temporal localization, demonstrating that localization of the manipulated segment provides richer supervision than binary clip-level labels alone. Mehta et al. [25] introduced HFMF, a hierarchical fusion architecture that combines multi-stream feature extraction with scale-wise cross-modal integration, capturing both fine-grained lip-sync discrepancies and global audio-visual rhythm mismatches. Liu et al. [26] surveyed the evolution from single-modal to multimodal deepfake detection, identifying audio-visual temporal synchronization as the most reliable cue across manipulation types and highlighting open challenges in real-world deployment. III. M ETHODOLOGY This research employs a framework for deepfake detection based on temporal artifact analysis using 3D Convolutional Neural Networks with specialized preprocessing and transfer learning strategies informed by recent advances in video analysis [11]. Our approach integrates video preprocessing, face detection and tracking, temporal feature extraction using R3D-18 architecture, and classification optimized for detecting inconsistencies across video frame sequences following contemporary best practices in 3D CNN design [4]. The methodology focuses specifically on facial video analysis with attention to temporal dynamics that reveal manipulation artifacts invisible in single-frame analysis, implementing a multi-stage pipeline with optimizations at each stage following recent developments in spatiotemporal generalization [5]. The complete workflow is illustrated in Figure 1, showing the progression from raw video input through preprocessing, temporal sequence extraction, 3D CNN analysis, and final classification based on current approaches to temporal deepfake detection [6]. A. Problem Formulation We frame deepfake detection as binary video classification. Given an input clip x ∈ RT ×H×W ×3 consisting of T consecutive RGB frames at resolution H × W , we learn a parameterized classifier fθ : RT ×H×W ×3 → [0, 1] that outputs the probability of the clip being synthetic. Training minimizes a composite objective: L = LBCE + λ Ltc ,
(1) where LBCE = − y log fθ (x) + (1 − y) log(1 − fθ (x)) is the binary cross-entropy on label y ∈ {0, 1} and Ltc is a
temporal-consistency regularizer that penalizes large framewise variation of intermediate feature maps ϕt (x): Ltc =
T −1 1 X ∥ϕt+1 (x) − ϕt (x)∥22 . T − 1 t=1
(2)
We set λ = 0.1 unless otherwise noted; an ablation is reported in Section IV. B. Dataset The research primarily utilizes the DeepfakeTIMIT dataset, which provides a controlled environment for evaluating deepfake detection methods across different quality levels and manipulation techniques. This dataset consists of 620 videos (588 GAN-synthesized fakes and 32 original real recordings) from 32 subjects (16 similar-looking pairs), generated using a GAN-based approach derived from the original autoencoderbased DeepFake algorithm with consistent backgrounds and lighting conditions. The dataset contains both lower quality (64×64) and higher quality (128×128) deepfakes, allowing evaluation of detection performance across varying quality levels and assessment of sensitivity to the different visual fidelities commonly encountered in social media environments. The videos feature known and consistent generation methods based on a standardized GAN-based approach, providing a reliable basis for identifying temporal artifacts that are reproducible and characteristic of the manipulation technique. The dataset includes sufficient scale and diversity with 620 videos distributed across 32 subjects arranged in 16 similar-looking pairs, enabling reliable model training and evaluation while maintaining statistical significance in experimental results. The relatively short video duration of 4-6 seconds provides an optimal balance between capturing sufficient temporal information for artifact detection while maintaining computational efficiency during training and evaluation. For cross-dataset validation, we also evaluate our approach on a subset of the FaceForensics++ dataset, specifically focusing on DeepFakes and Face2Face manipulations, to assess generalization performance across different manipulation techniques. The dataset characteristics are summarized in Table II and detailed statistical analysis is highlighting the key properties that make it suitable for temporal artifact analysis. C. Detailed Methodology Our methodology encompasses a multi-stage pipeline for temporal artifact detection in deepfake videos, beginning with specialized video preprocessing techniques that prepare videos for analysis through frame extraction, normalization, and quality standardization specifically optimized for detecting temporal artifacts in facial videos. Each video is processed to extract evenly spaced frame sequences regardless of original video length, ensuring consistent temporal analysis across all samples while maintaining the integrity of temporal relationships between consecutive frames. We apply histogram equalization and adaptive contrast enhancement to mitigate quality variations that might affect detection performance,
MULTIMODAL FUSION NETWORKS FOUNDATION PROPOSED MODELS ADVANCED CNN ARCHITECTURES FREQUENCY DOMAIN ANALYSIS
TRANSFORMER-BASED TEMPORAL MODELING TECHNIQUE GROUP
TABLE I S UMMARY OF RECENT DEEPFAKE - DETECTION METHODS RELEVANT TO THIS WORK .
Author Zhang et al. [3]
Method Transformer
Nguyen et al. [2]
ViT
Nguyen et al. [13] Yan et al. [5]
FakeFormer
Wang et al. [27]
Survey ViT
Nguyen al. [28] Li et al. [29]
Self-sup ViT
et
Usmani et al. [14] Kingra et al. [15]
Plug-and-play
Sequential Trans STKD-VViT SFormer
Zakkam et al. [16] Chen et al. [17]
CoDeiT
Yan et al. [18]
DF40
Oorloff et al. [23]
AVFF
Katamneni et al. [24] Mehta et al. [25]
Cross-modal
Liu et al. [26]
AV survey
Tan et al. [19]
Freq-aware
Chen et al. [20]
Luo et al. [22]
3D trajectories WaveletCLIP Freq-masking
Chu et al. [4]
3D CNN
Cavia et al. [11]
Real-time CNN Multimodal CNN FAME
Baru et al. [21]
Gandhi et al. [6] Ahmad et al. [9]
Spatio-temp
HFMF
Srivasthav et al. [7] Lanzino et al. [10] Fang et al. [12]
Meta-learning
Han et al. [1]
Foundation model 3D CNN
Proposed
Binary CNN UniForensics
Technique Natural consistency repr. Vulnerabilityaware Efficient transformers Spatiotemporal adapter ViT survey
Dataset FF++, DFDC
Acc. N/A
Contribution Temporal consistency
Limitation Video requirement
FF++, DFDC
N/A
FF++, DFDC
N/A
FF++, DFDC
N/A
Multiple
N/A
Architectural complexity Domain dependency Fine-tuning required Review only
Comparative analysis Texture-shapeorder Knowledge distillation End-to-end spatio-temp Contrastive efficient Consistency + attention Next-gen benchmark Audio-visual fusion Contextual attention Hierarchical fusion Progress and challenges Frequency space learning Spatiotemporal traj. Wavelet + CLIP ViT Spatial interaction Reduced spatial dep. Patch-level detect. Combined features Model attribution
FF++, DFDC
N/A
Generalizable detection Real-time optimization Cross-domain transfer Transformer survey Self-supervised
FF++, DFDC
N/A
Multi-cue design
FakeAVCeleb
96.0%
FF++, CelebDF DFDC, Celeb-DF Multiple
N/A
Efficient distillation Streamlined pipeline Data efficiency
40 methods
N/A
FakeAVCeleb
N/A
AV datasets
N/A
DFDC, FF++
N/A
Multiple
N/A
Cross-modal attention Detection + localization Scale-aware analysis AV survey review
FF++, DFDC
N/A
Generalizability
FF++, DFDC
N/A
FF++, Diffusion DFDC, FF++
N/A
FF++, DFDC
N/A
WildRF, DFDC FF++, DFDC
N/A
DFDM, FF++
N/A
Adaptive multiagent Real-time inference General facial repr. Facial comp. guided Transfer + temporal
Multiple
N/A
DFDC, DF
N/A
FF++, DFDC
N/A
Multiple
N/A
TIMIT, FF++
94.2%
Compression robust Cross-dataset generaliz. Frequencyspatial fusion Better generalization Real-world evaluation Multi-stream integration Source identification Cross-domain adapt. Extreme efficiency Unified framework Foundation adaptation Temporal artifacts
N/A N/A
N/A
N/A
Advanced consistency Broad evaluation
Limited labeled data Design complexity Distillation overhead Architectural complexity Limited generalization Computational cost Emerging techniques Audio requirement Complex training Computational overhead Survey only Frequency limited Trajectory complexity CLIP dependency Processing complexity Video requirement Domain gap Design complexity Limited to faceswap Data drift handling Binary quantization Representation breadth Resource intensive Video dependency
13 Normalization ImageNet Statistics
3D CNN Model R3D-18 Architecture
11
12
Temporal Feature Extraction
14
10
1
Temporal Sampling 16 frames per clip
9
15
Prediction
Data Augmentation Random Crop, Flip, Color Jitter
No Face
Frame Executio
Input Video
Use Full Frame
4
2
Classification Layer Real vs Fake
Frame Detection
8 Face Found
3
17
Resize to 128x128
Real
Output: Real Video
5
6
Fake 16
Output: Deepfake Detected
7 Face Cropping with 30% Margin
Fig. 1. Complete workflow pipeline for temporal deepfake detection showing video preprocessing, face detection and tracking, temporal sequence extraction, 3D CNN feature extraction using R3D-18 architecture, and binary classification. The pipeline processes 16-frame sequences through specialized 3D convolutions to capture spatio-temporal inconsistencies characteristic of deepfake manipulations.
1 Video Input
2 Data Preprocessing 2 . 1 Face Detect ion Haar Cascade
2 .3
Resize & Normalize
3
Model Processing
Face Cropping
Frame Extraction
Confidence Score
Convolutional Neural Network (CNN) 3 .1
2 .2
4
3D Convolution Layers
3 .2
Temporal Feature Maps & Spatial-Temporal Pooling
3 .3
Fully Connected Layers
3 .4
Binary Classification
Fig. 2. Detailed workflow showing user input and processing pipeline for inference from the model. The inference pipeline consists of video preprocessing, face detection and tracking, temporal sequence extraction, and feature processing steps that prepare videos for 3D CNN analysis and temporal artifact detection. It is then followed by giving the preprocessed images to the trained 3D CNN model, which then gives back to the user the Confidence scores.
particularly important for lower-quality videos where manipulation artifacts may be less pronounced due to compression or resolution limitations. The preprocessing pipeline includes temporal alignment procedures that account for slight variations in frame rates and ensure that the 16-frame sequences we extract maintain consistent temporal spacing for optimal 3D CNN processing. Additionally, we implement quality assessment metrics during preprocessing to filter out corrupted frames or sequences that might negatively impact training or evaluation performance. The normalization procedures are carefully designed to preserve subtle temporal variations that serve as detection signals while standardizing overall brightness and contrast levels across different video sources. Finally, we apply specialized face detection and tracking algorithms
during preprocessing using Haar cascade-based detectors to ensure that facial regions are consistently centered and aligned across frame sequences, which is crucial for the temporal consistency analysis performed by our 3D CNN architecture. The core of our approach is a specialized 3D CNN based on the R3D-18 architecture, optimized for capturing temporal inconsistencies in facial videos through simultaneous modeling of spatial and temporal features. We employ 3D convolutions to enable the network to identify artifacts that manifest across frame sequences rather than within individual frames, incorporating residual connections to facilitate deeper network training and capture more complex temporal patterns across longer sequences. Our model processes 16frame sequences through multiple convolutional blocks, each
1
Face Frame Extract
TABLE II D EEPFAKE TIMIT DATASET CHARACTERISTICS AND STATISTICS . W E USE A RESTRICTED SUBSET; THE FULL DATASET CONTAINS 320 REAL AND 640 FAKE VIDEOS (320 LOW- QUALITY, 320 HIGH - QUALITY ). O UR SUBSET EXCLUDES CORRUPT FILES AND RETAINS 588 FAKE AND 32 REAL VIDEOS AFTER QUALITY FILTERING . Dataset Feature Total videos Original (real) videos Fake videos Quality variants Video duration Frame rate Subjects Face swapping technique Background control Lighting conditions Temporal resolution Cross-validation split
Fig. 3. DeepfakeTIMIT dataset organization showing the distribution of real and fake videos across quality levels (64×64 and 128×128), subject pairs, and temporal characteristics. The dataset provides controlled conditions for evaluating temporal artifact detection methods across different video quality levels.
Fig. 4. Sample frames from DeepfakeTIMIT dataset showing authentic (top row) and manipulated (bottom row) facial videos across different quality levels. The comparison highlights subtle differences that require temporal analysis for reliable detection.
designed to extract increasingly complex spatio-temporal features, while the 3D convolutions enable simultaneous analysis of spatial patterns within frames and temporal patterns across frame sequences. The temporal learning component specifically targets inconsistencies in facial motion patterns, eye blinking sequences, and micro-expression transitions that are characteristic of manipulated videos, using the natural temporal coherence present in authentic facial movements. We initialize our model with weights pre-trained on the Kinetics-400 action recognition dataset, providing a strong foundation for temporal feature extraction that uses knowledge of natural human motion patterns that can be adapted to identify unnatural patterns characteristic of manipulated videos. The transfer learning approach is particularly beneficial
Value 620 32 588 2 (HQ: 128×128, LQ: 64×64) 4-6 seconds 25 FPS 32 (16 pairs) GAN-based Consistent Controlled Adequate for artifact detection 70% train, 15% val, 15% test
as the pre-trained model already encodes knowledge about natural human movements, facial dynamics, and temporal consistency patterns, which forms an excellent foundation for detecting manipulation artifacts. We apply a sophisticated finetuning strategy that gradually unfreezes layers during training, starting with the final classification layers and progressively allowing earlier layers to adapt to the specific characteristics of facial manipulation detection while preserving valuable temporal features learned from action recognition. Our training procedure incorporates several optimizations to enhance model performance and generalization, ensuring reliable detection across various deepfake types and quality levels through extensive data augmentation and curriculum learning strategies. First, we employ a balanced sampling strategy to address class imbalance between real and fake videos, implementing weighted sampling that ensures equal representation of both classes during each training epoch while preventing the model from developing bias toward the more numerous fake video class. Second, we apply extensive data augmentation techniques including random cropping, horizontal flipping, temporal jittering, and frame rate variation to increase training data diversity and improve the model’s stability under various presentation conditions encountered in realworld scenarios. Third, we implement a curriculum learning approach that gradually introduces more challenging examples during training, starting with clear, high-quality examples and progressively incorporating more subtle manipulations and lower-quality videos as the model develops stronger detection capabilities. Fourth, we utilize a specialized loss function that combines standard cross-entropy classification loss with a temporal consistency term that penalizes inconsistent predictions across neighboring frames, encouraging the model to learn temporally coherent features characteristic of authentic or manipulated content. Fifth, we employ advanced optimization techniques including learning rate scheduling with warm-up periods, gradient clipping to prevent training instability, and early stopping with patience to prevent overfitting while ensuring optimal convergence. We run training with five random
seeds to ensure reproducibility; reported metrics are averaged across these runs. D. Evaluation Metrics We employ a set of evaluation metrics specifically designed to assess different aspects of temporal deepfake detection performance, providing thorough analysis of model capabilities across various scenarios and quality levels. Accuracy measures the proportion of correctly classified videos in the test set, providing a general measure of detection performance calculated as shown in Equation 3, where TP represents true positives (correctly identified deepfakes), TN represents true negatives (correctly identified authentic videos), FP represents false positives (authentic videos incorrectly classified as deepfakes), and FN represents false negatives (deepfakes incorrectly classified as authentic). TP + TN (3) TP + TN + FP + FN Precision measures the proportion of videos classified as deepfakes that are actually manipulated, providing insights into the reliability of positive predictions as defined in Equation 4, which is particularly important for content moderation applications where false positives can impact legitimate content creators. Accuracy =
TP (4) TP + FP Recall measures the proportion of actual deepfakes that are correctly identified, assessing the model’s ability to find all manipulated videos as shown in Equation 5, which is crucial for security applications where missing deepfakes can have serious consequences. P recision =
TP (5) TP + FN F1-Score provides a balanced measure of detection performance by computing the harmonic mean of precision and recall as defined in Equation 6, offering a single metric that considers both false positives and false negatives. Recall =
F1 = 2 ×
P recision × Recall P recision + Recall
(6)
E. Experimental settings All experiments were conducted in a standardized environment to ensure reproducibility and fair comparison with baseline methods, using identical preprocessing pipelines and evaluation protocols across all approaches. For comparative evaluation, we implemented several baseline methods under identical conditions, including a frame-by-frame 2D CNN approach using XceptionNet that analyzes individual frames independently and aggregates results, a CNN+LSTM approach that extracts features from individual frames with a CNN and then processes the sequence with an LSTM, an optical flow-based method that analyzes motion patterns between consecutive frames, and a frequency domain analysis approach
that examines discrepancies in the frequency spectrum of video frames. All models were trained on the same data split (70% training, 15% validation, 15% testing) with identical preprocessing to ensure fair comparison, and we conducted five training runs with different random initializations for each model to assess the stability of results. The experimental environment utilized NVIDIA RTX 3090 GPUs with 24GB memory, PyTorch 1.12 framework, and CUDA 11.6 for GPU acceleration, ensuring consistent computational resources across all experiments. Hyperparameter optimization was performed using grid search for learning rates (1e-5 to 1e-3), batch sizes (4 to 16), and dropout rates (0.3 to 0.7), with early stopping patience set to 10 epochs and cosine annealing learning rate scheduling. Cross-validation was performed using 5fold stratified sampling to ensure balanced representation of real and fake videos across all folds, and statistical significance testing was conducted using paired t-tests with p ¡ 0.05 to validate performance differences between methods. TABLE III H YPERPARAMETER CONFIGURATION AND EXPERIMENTAL SETTINGS FOR THE 3D CNN TEMPORAL DEEPFAKE DETECTION MODEL SHOWING THE NETWORK CONFIGURATION USED IN THIS STUDY. Network Configuration Epochs 50 Learning rate 1e-4 Mini batch size 8 Optimizer Adam Momentum 0.9 Weight decay 1e-5 Dropout rate 0.5 Frame sequence length 16 Input resolution 128×128 Data augmentation Yes (crop, flip, temporal) Learning rate schedule Cosine annealing Early stopping patience 10 epochs Loss function Cross-entropy Transfer learning source Kinetics-400 GPU memory usage 18GB (RTX 3090) Training time per epoch 12 minutes
IV. R ESULTS Our temporal artifact analysis approach using 3D CNNs demonstrates superior performance in deepfake detection compared to frame-by-frame analysis methods, with particularly strong results on high-quality deepfakes that are typically more challenging to detect using traditional spatial methods. The proposed 3D CNN approach achieves 94.2% accuracy on the DeepfakeTIMIT test set, significantly outperforming the frame-by-frame XceptionNet baseline (89.7%) and other comparative methods including CNN+LSTM (91.3%) and optical flow analysis (87.6%). This performance advantage is particularly pronounced for high-quality (128×128) deepfakes, where our approach maintains 92.8% accuracy compared to the XceptionNet baseline’s 84.5%, demonstrating the effectiveness of temporal analysis for detecting sophisticated manipulations. The experimental results confirm our hypothesis that temporal inconsistencies provide valuable detection signals that complement spatial artifact analysis, with the 3D CNN
16×128×128×3
4×32×32×64
Temporal Consistency Analysis
Spatio-temporal Feature Extraction
Conv3D 7×7×7 ReLU
2×16×16×128
MaxPool3D 3×3×3
ResBlock 3×3×3 64 filters
1×4×4×512
Deep Temporal Pattern Recognition
ResBlock 3×3×3 128 filters
ResBlock 3×3×3 256 filters
ResBlock 3×3×3 512 filters
512
2
Feature Aggregation
Binary Classification
Global AvgPool
FC Layer Dropout
Softmax Output
Real/Fake 512 2 512
Input Video 16×128×128×3
4×32×32×64 4×32×32×64 8×64×64×64
2×16×16×128 1×8×8×256 1×4×4×512
Transfer Learning from Kinetics-400 Pre-trained on action recognition
Temporal Artifact Detection Captures inconsistencies across 16 frames
Fig. 7. Layer-wise structure of the R3D-18 architecture used for deepfake detection, showing 3D residual blocks, temporal pooling, and the final classification head. Input dimensions and feature-map sizes at each stage are annotated to illustrate the spatio-temporal compression across the network depth.
architecture effectively capturing subtle artifacts that manifest across frame sequences rather than within individual frames. Analysis of detection performance across different deepfake quality levels reveals that our approach maintains more consistent performance than baseline methods as quality increases, with significantly smaller performance degradation (1.4 percentage points) compared to spatial methods. Cross-dataset evaluation demonstrates promising generalization capabilities, with our model achieving 76.4% accuracy on FaceForensics++ without fine-tuning, indicating superior transfer compared to frame-by-frame baselines whose cross-dataset accuracy peaks at 70.8%. The performance comparison is showing consistent superiority across all evaluation metrics, with detailed ablation study results presented in Figure 8. Analysis of the learned temporal features shows that specific inconsistency types provide strong detection signals, as visualized in Figure 9. Eye blinking patterns show the highest discriminative power among all temporal features, with unnatural timing, frequency, and synchronization being consistently detectable across quality levels due to the difficulty of generating realistic blinking sequences that maintain proper temporal coordination with speech and facial expressions. Microexpression transitions, particularly around the mouth and eyes
Fig. 8. Detailed ablation study results showing the contribution of different components to the overall detection performance. The chart demonstrates the impact of transfer learning, face cropping, temporal sequence length, and other architectural choices on accuracy across different quality levels.
during speech, exhibit temporal artifacts that are difficult for
deepfake methods to accurately reproduce while maintaining the subtle timing and coordination that characterizes natural facial expressions. Head movement and pose consistency across frames shows distinctive patterns in manipulated videos, with our model effectively capturing subtle motion irregularities that result from the frame-by-frame generation process used in many deepfake techniques. The temporal progression of facial hair, eyebrow positioning, and other fine-grained facial features often exhibits subtle inconsistencies in deepfakes that our model successfully learns to detect, contributing to the consistency of our temporal analysis approach. Speech-related mouth movements demonstrate temporal inconsistencies in deepfakes, where the coordination between lip movements and the underlying facial structure often fails to maintain the natural temporal relationships present in authentic speech. These findings identify the most reliable temporal artifacts and quantify their discriminative power across manipulation techniques and quality levels.
due to insufficient temporal context and longer sequences providing no significant improvement while substantially increasing computational cost. Cross-dataset and quality-level results are summarized in Table IV. Comparison with baseline methods reveals that our 3D CNN approach consistently outperforms frame-by-frame analysis methods across different quality levels and datasets, confirming the value of explicitly modeling temporal inconsistencies in deepfake videos [30]. The CNN+LSTM baseline, which represents a simple integration of spatial and temporal analysis, outperforms the pure spatial method (XceptionNet) [8] but falls short of our specialized 3D CNN approach, suggesting that sophisticated spatiotemporal modeling is crucial for optimal performance [31]. The results show that temporal analysis significantly enhances detection performance, especially through dedicated 3D CNN architectures rather than simple sequential processing [32]. Performance Comparison: Different Deepfake Detection Methods
Performance Across Different Quality Levels and Datasets
4.5% improvement over best baseline
100
89.7%
100
94.2%
91.3% 87.6%
85.1%
60
40
20
Baseline (2D CNN) Proposed92.8% (3D CNN)
84.5%
80
Detection Accuracy (%)
Detection Accuracy (%)
80
76.4%
60
40
20
Key Insight: Temporal analysis provides consistent improvement across all scenarios 0
94.2%
92.5%
89.2%
Frame-by-Frame (2D CNN)
CNN+LSTM
Optical Flow
Superior generalization capability with 16.4% improvement on cross-dataset
Frequency Domain Proposed R3D-18 (3D CNN)
0
Low Quality (64×64)
High Quality (128×128)
Cross-Dataset (FF++)
Fig. 10. Temporal artifact visualization showing specific frame sequences where the model successfully detects manipulation artifacts invisible to singleframe analysis. Heat maps highlight regions of temporal inconsistency that contribute to detection decisions. Fig. 9. Temporal artifact visualization showing specific frame sequences where the model successfully detects manipulation artifacts invisible to singleframe analysis. Heat maps highlight regions of temporal inconsistency that contribute to detection decisions.
Ablation studies assess each component’s contribution to temporal artifact detection, as shown in Figure 8. Transfer learning from Kinetics-400 provides a significant boost (+7.2 percentage points) compared to training from scratch, confirming the substantial value of motion-related pre-training for understanding natural temporal patterns in facial movements and demonstrating the effectiveness of our transfer learning strategy. Face cropping and tracking improve performance by 3.5 percentage points compared to using full frames, demonstrating the critical importance of focusing computational resources on the manipulated facial regions rather than processing entire video frames that may contain irrelevant background information. The proposed temporal sequence length of 16 frames achieves optimal performance after systematic evaluation of sequence lengths ranging from 8 to 32 frames, with shorter sequences reducing accuracy by 2.7 points
V. D ISCUSSION The experimental results demonstrate that temporal artifact analysis provides a powerful approach for deepfake detection, with several key insights emerging from our research that directly address the research questions posed in this study. The R3D-18 results confirm that 3D CNNs capture temporal inconsistencies across frame sequences, detecting artifacts invisible to single-frame methods. The significant performance gap between our approach and frame-by-frame methods (+4.5 percentage points over XceptionNet) demonstrates the value of spatio-temporal modeling for this task, validating our hypothesis that temporal analysis provides detection signals that are complementary to spatial approaches. The ablation studies further reveal that transfer learning from action recognition provides a substantial performance boost, with the pre-trained model already encoding knowledge about natural human movements that can be used to identify manipulation artifacts. Our 3D CNN model consistently outperforms frameby-frame analysis methods across different quality levels and
TABLE IV P ERFORMANCE OF DEEPFAKE DETECTION METHODS ON THE D EEPFAKE TIMIT TEST SET. C ROSS -DS: ZERO - SHOT CROSS - DATASET ACCURACY ON FACE F ORENSICS ++. HQ-ACC .: ACCURACY ON 128×128 HIGH - QUALITY SUBSET ONLY. Method
Dataset
XceptionNet CNN+LSTM Optical Flow Frequency Domain Proposed 3D CNN
DeepfakeTIMIT DeepfakeTIMIT DeepfakeTIMIT DeepfakeTIMIT DeepfakeTIMIT
Acc. (%) 89.7 91.3 87.6 88.9 94.2
Prec. (%) 88.3 90.2 85.9 87.1 93.5
datasets, confirming the value of explicitly modeling temporal inconsistencies in deepfake videos rather than relying solely on spatial artifact detection. The architecture’s ability to process 16-frame sequences through multiple convolutional blocks enables extraction of increasingly complex spatio-temporal features that capture subtle manipulation signatures invisible to frame-by-frame analysis. The integration of residual connections facilitates deeper network training and enables the capture of more complex temporal patterns across longer sequences, contributing to the superior performance observed in our experiments. Feature analysis identifies specific temporal artifacts that provide the most reliable detection signals, exposing fundamental weaknesses of current deepfake generation methods. Eye blinking patterns emerge as particularly strong indicators, with deepfake models often struggling to maintain natural blinking frequency and consistency across frames due to the difficulty of coordinating these physiological signals with facial expressions and speech patterns. Micro-expression transitions around the mouth and eyes during speech show distinctive artifacts, as these subtle movements are difficult to accurately synthesize while maintaining temporal coherence and natural timing relationships. Head movement consistency and the associated changes in lighting and shadows provide additional temporal cues that contribute to detection performance, as these environmental factors are often not properly maintained across frame sequences in generated content. The temporal progression of facial hair, eyebrow positioning, and other fine-grained facial features often exhibits subtle inconsistencies in deepfakes that our model successfully learns to detect. This class of features remains detectable even when other artifacts have been suppressed, making it a useful fallback signal. These findings suggest that future deepfake detection systems should specifically target these artifact categories, potentially with specialized models for each type of inconsistency to maximize detection effectiveness. The quantitative analysis of temporal features reveals that certain inconsistencies remain present even in highly sophisticated deepfakes, providing persistent detection signals that are difficult for generation methods to eliminate completely. Cross-quality performance analysis reveals that temporal artifacts remain more consistent across quality levels than the spatial artifacts targeted by frame-by-frame methods. While all methods show some performance degradation for high-quality deepfakes, the drop is significantly smaller for our 3D CNN
Rec. (%) 91.4 92.7 89.8 90.5 95.1
F1 (%) 89.8 91.4 87.8 88.8 94.3
AUC (%) 94.2 95.7 93.1 93.8 97.6
EER (%) 10.8 8.5 12.3 11.2 6.3
Cross-DS (%) 67.3 70.8 64.9 66.1 76.4
HQ-Acc. (%) 84.5 87.6 83.2 82.1 92.8
approach (1.4 percentage points) compared to spatial methods (5.2 points for XceptionNet), suggesting that temporal inconsistencies remain a more reliable detection signal as deepfake generation technology improves. The CNN+LSTM baseline, which represents a simple integration of spatial and temporal analysis, outperforms the pure spatial method (XceptionNet) but falls short of our specialized 3D CNN approach, indicating that sophisticated spatio-temporal modeling is crucial for optimal performance. Our comparative evaluation demonstrates that temporal analysis complements spatial approaches, with the combined capabilities of 3D CNNs achieving higher performance than either approach alone when properly integrated through dedicated architectures. The superior cross-dataset generalization capabilities demonstrated in our evaluation provide evidence that temporal analysis offers more universal detection signals compared to spatial methods that may be specific to particular generation techniques. This suggests that future work should explore more sophisticated integration strategies that use the strengths of both spatial and temporal analysis while addressing their respective limitations through unified architectures. The transfer learning results confirm that action recognition models provide beneficial initialization for temporal analysis in deepfake detection. The significant performance boost from Kinetics-400 pre-training (+7.2 percentage points) shows that general motion understanding transfers effectively to facial movement analysis. This finding is particularly significant because it suggests that future work could explore more specialized pre-training on facial movement datasets to further enhance transfer learning for this specific task, potentially leading to even greater performance improvements. The pretrained model’s understanding of natural human motion dynamics provides a crucial foundation for detecting unnatural patterns in deepfake videos, as evidenced by the consistent performance improvements observed across all evaluation metrics. The gradual fine-tuning strategy we employed successfully adapts the pre-trained features to the specific characteristics of facial manipulation detection while preserving valuable temporal feature extraction capabilities learned from action recognition. Our cross-dataset evaluation results demonstrate promising generalization capabilities, with strong performance after minimal fine-tuning on new datasets, addressing one of the key limitations of existing methods that often perform poorly when confronted with novel manipulation techniques.
The ability to use temporal consistency as a more universal property of authentic videos, rather than relying on specific artifact patterns that may vary across manipulation methods, contributes to this improved generalization and suggests broader applicability for real-world deployment scenarios. When compared with existing contemporary methods, our approach demonstrates several advantages that position it favorably in the current landscape of deepfake detection research. The performance improvements over recent transformer-based approaches are particularly noteworthy, as our 3D CNN method achieves comparable or superior accuracy while requiring significantly less computational resources during inference, making it more suitable for real-time applications. Compared to self-supervised learning approaches that require extensive unlabeled data for pre-training, our transfer learning strategy uses existing action recognition models, reducing training time and data requirements while achieving superior performance on benchmark datasets. Our method shows competitive performance with multimodal fusion techniques while operating solely on visual information, showing that temporal analysis alone achieves reliable detection without requiring synchronized audio streams that may not be available in all application scenarios. The frequency domain analysis methods show strong performance on compressionresistant artifacts, but our temporal approach provides broader coverage of manipulation types and demonstrates superior generalization across different quality levels and manipulation techniques. Recent work on transformer architectures and selfsupervised learning has shown strong results, but our 3D CNN approach offers a more efficient solution that achieves comparable performance with lower computational overhead and simpler implementation requirements. The practical implications of this work extend beyond academic contribution to address real-world challenges in media authentication and content moderation, with several important considerations for deployment in social media and security applications. Our methodology’s ability to detect high-quality deepfakes that evade traditional approaches makes it useful for content moderation systems that must handle the increasing sophistication of manipulated content shared on social platforms. The computational efficiency of our approach compared to transformer-based methods makes it more suitable for large-scale deployment scenarios where processing millions of videos requires efficient algorithms that can operate within reasonable resource constraints. The cross-dataset generalization capabilities provide confidence that the method can adapt to new manipulation techniques as they emerge, reducing the need for frequent model retraining and maintaining detection effectiveness as the threat landscape evolves. However, the requirement for video input limits applicability to scenarios where only still images are available, and the focus on facial manipulations may not cover other types of synthetic media that are becoming increasingly prevalent. The temporal analysis framework we developed provides a foundation for future research that could expand to other manipulation types and integrate with complementary detection approaches for more
complete media authentication systems. A. Future Directions Several promising directions emerge from this research for advancing deepfake detection capabilities and addressing the evolving landscape of synthetic media manipulation in future work. Integration with physiological signals could provide even more reliable detection by combining temporal artifact analysis with physiological signal detection (pulse, blinking, breathing patterns), as these biological signals are particularly difficult for deepfake methods to accurately reproduce while maintaining temporal consistency. Developing advanced visualization techniques to highlight the specific temporal inconsistencies identified by the model would significantly improve interpretability and trust in detection results, which is crucial for applications in legal contexts, journalistic verification, or content moderation decisions. Adversarial training against newer generators could improve resilience to evolving manipulation techniques. Extending the temporal analysis approach to incorporate audio-visual inconsistencies could provide additional detection signals for deepfakes that manipulate both visual content and speech patterns, creating more complete multimodal detection systems. Exploring transformer-based architectures for temporal analysis could potentially improve performance while maintaining computational efficiency, and investigating self-supervised learning approaches could reduce dependence on labeled training data while improving generalization to novel manipulation techniques. Finally, developing real-time optimization strategies and edge computing implementations could enable deployment on mobile devices and browsers, making temporal deepfake detection accessible for widespread social media content moderation and personal verification applications. VI. L IMITATIONS Despite the promising results, our approach has several limitations that present opportunities for future research and highlight important considerations for practical deployment in real-world scenarios. First, the computational requirements of 3D CNNs are substantially higher than frame-by-frame methods, potentially limiting real-time application on resourceconstrained platforms such as mobile devices or browser-based social media environments where immediate detection may be crucial for content moderation. Second, our approach requires video input and cannot be applied to single images, limiting its applicability in scenarios where only still images are available for analysis, such as profile pictures or static social media posts that may also be manipulated. Third, while our method performs well on face-swapping deepfakes generated using GAN-based approaches, its effectiveness on other manipulation types like face reenactment, attribute manipulation, or newer generation techniques has not been evaluated across diverse datasets. Fourth, our current implementation assumes relatively stable facial positioning within the video, with performance potentially degrading for videos with extreme head movements, rapid scene changes, or significant occlusions
that disrupt the temporal consistency analysis. Finally, the approach’s performance may be affected by video compression artifacts, frame rate variations, or quality degradation that commonly occurs during social media sharing, requiring additional stability considerations for deployment scenarios where video quality cannot be controlled. VII. C ONCLUSION This research successfully demonstrates that temporal artifact analysis using 3D Convolutional Neural Networks provides a powerful and effective approach for deepfake detection that significantly advances the state-of-the-art in synthetic media analysis. Our specialized R3D-18 architecture, enhanced with transfer learning from action recognition models, achieves 94.2% accuracy on DeepfakeTIMIT and 92.8% on high-quality intra-dataset evaluation, with 76.4% cross-dataset transfer to FaceForensics++, representing substantial improvements over existing frame-by-frame methods and confirming the value of explicitly modeling temporal inconsistencies in deepfake videos. Our experimental evaluation reveals that specific temporal artifacts, particularly eye blinking patterns and micro-expression transitions, provide reliable detection signals that remain present even in highly sophisticated deepfakes, offering persistent indicators that are difficult for generation methods to eliminate. The superior cross-quality performance and generalization capabilities demonstrated in our evaluation provide evidence that temporal analysis yields more consistent, universal detection signals than spatial methods, making it better suited for real-world deployment scenarios where video quality and manipulation techniques may vary significantly. The temporal artifact analysis framework we developed not only contributes to the immediate challenge of deepfake detection but also provides a foundation for future research in synthetic media authentication, with practical implications for content moderation, media verification, and forensic analysis applications. Our findings establish temporal inconsistency analysis as a crucial component for next-generation deepfake detection systems and provide clear directions for future research that can address the evolving landscape of synthetic media manipulation in social media and beyond. R EFERENCES [1] Y.-H. Han, T.-M. Huang, K.-L. Hua, and J.-C. Chen, “Towards more general video-based deepfake detection through facial component guided adaptation for foundation model,” arXiv preprint arXiv:2404.05583, 2024. [2] D. Nguyen, M. Astrid, A. Kacem, E. Ghorbel, and D. Aouada, “Vulnerability-aware spatio-temporal learning for generalizable deepfake video detection,” arXiv preprint arXiv:2501.01184, 2025. [3] D. Zhang, Z. Xiao, S. Li, F. Lin, J. Li, and S. Ge, “Learning natural consistency representation for face forgery video detection,” arXiv preprint arXiv:2407.10550, 2024. [4] B. Chu, X. Xu, Y. Zhang, W. You, and L. Zhou, “Reduced spatial dependency for more general video-level deepfake detection,” arXiv preprint arXiv:2503.03270, 2025. [5] Z. Yan, Y. Zhao, S. Chen, M. Guo, X. Fu, T. Yao, S. Ding, and L. Yuan, “Generalizing deepfake video detection with plug-and-play: Video-level blending and spatiotemporal adapter tuning,” arXiv preprint arXiv:2408.17065, 2024.
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