arXiv CS
open access
MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition
Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings. We propose MIG-COW, a novel adversarial attack framework that generates highly generalizable and visually imperceptible adversarial examples. By combining momentum-int
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
- MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition#1424032
- Neither Adversarial Training Nor Purification: Emergent Adversarial Robustness from Oscillatory Predictive Learning#986014
- Automatic Red Teaming for Implicit Vulnerabilities of Text-to-Image Models#973701
- PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models#303213
- Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability#1014642
Record · ID 1417767
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