Xinlei He

The paper introduces SADBench, a systematic benchmark designed to evaluate both the effectiveness of steganographic attacks injecting harmful content and the robustness of steganalysis defenses against these threats.

The paper systematically measures the risk of current image-to-3D models generating harmful geometries, finding that these models are effective at reconstruction and existing safeguards are insufficient.

The paper introduces MARS, a novel meta-adversarial framework that significantly improves black-box adversarial attacks against state-of-the-art Singing Voice Deepfake Detection (SVDD) systems by escaping the Linearity Trap.

The paper introduces BadBone, a stealthy and adaptive backdoor attack that compromises a backbone model specifically to target downstream tasks utilizing prompt learning, demonstrating high attack success rates against state-of-the-art defenses.

The paper introduces a novel Clean-Referenced Feature-Vocoder Attack, a black-box adversarial attack that perturbs high-level SSL feature representations instead of raw audio waveforms, achieving superior transferability and robustness against modern ASR defenses.