Hugging Face Trending Papers

Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples

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The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images.

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arXiv AI
3d ago

Let the Carrier Carry the Attack: Preserving the Subject in Adversarial Image Generation

The paper introduces a "carrier"—a secondary visual element—to help preserve the main subject of an image during strong, unrestricted adversarial attacks. By allocating a larger portion of globally normalized attack updates to the carrier, the method reduces distortion of the subject while maintaining attack strength. Additionally, the carrier enhances cross-model transferability and allows targeted attacks to mislead classifiers while keeping the subject recognizable to humans.

By Linfeng Jiang, Steven McDonagh, Yuhang Chen, Xingyu Zhao, Siddartha Khastgir, Andi Zhang
arXiv AI
Aug 14

SPARED: Reasoning-Based AI-Generated Image Detection via Adversarially Edited Data

arXiv:2608. 12876v1 Announce Type: cross Abstract: Detecting AI-generated images is only half the task: a deployed detector must also justify its verdict, yet existing detectors inherit three failure modes from their training data: real and fake images collected from different sources invite provenance shortcuts, supervised explanation corpora teach templated rationales, and a static forgery corpus leaves the decision boundary standing still while generators keep moving.

By Yicheng Bao, Xiahui Guo, Xuhong Wang, Xin Tan