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cs.CV2024
FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon
Deep neural networks are known to be vulnerable to security risks due to the inherent transferable nature of adversarial examples. Despite the success of recent generative model-ba…
cs.CV2024
Prompt-Driven Contrastive Learning for Transferable Adversarial Attacks
Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon
Recent vision-language foundation models, such as CLIP, have demonstrated superior capabilities in learning representations that can be transferable across diverse range of downstr…