arXiv (All)
open access
What Makes Adversarial Examples Transfer Across Deepfake Detectors?
artificial-intelligencecomputer-vision-and-pattern-recognitioncryptography-and-securitymachine-learning
Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred to a target model, unknown to the attacker. Yet how source--target compatibility shapes attack success remains poorly understood. Prior st
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Towards Generalizable Deepfake Detection via Real Distribution Bias Correction#1012153
- Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting#659136
- Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting#657000
- Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting#480012
- Neither Adversarial Training Nor Purification: Emergent Adversarial Robustness from Oscillatory Predictive Learning#986014
Record · ID 996800
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive