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Making Generated Images Hard To Spot: A Transferable Attack On Synthetic Image Detectors
Visually realistic GAN-generated images have recently emerged as an important misinformation threat.
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs
Understanding the transferability of adversarial attacks, i. e. an attacks ability to attack a different CNN than the one it was trained against, has important implications for designing CNNs that are resistant to attacks.
Defenses Against Multi-Sticker Physical Domain Attacks on Classifiers
In this paper, we propose new defenses that can protect against multi-sticker attacks.
A Transferable Anti-Forensic Attack on Forensic CNNs Using A Generative Adversarial Network
In this paper, we propose a new anti-forensic attack framework designed to remove forensic traces left by a variety of manipulation operations.
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