FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks
<|reference_start|>FACL-Attack: Frequency-Aware Contrastive Learning for Transferable Adversarial Attacks: 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-based attacks demonstrating strong transferability, it still remains a challenge to design an efficient attack strategy in a real-world strict black-box setting, where both the target domain and model architectures are unknown. In this paper, we seek to explore a feature contrastive approach in the frequency domain to generate adversarial examples that are robust in both cross-domain and cross-model settings. With that goal in mind, we propose two modules that are only employed during the training phase: a Frequency-Aware Domain Randomization (FADR) module to randomize domain-variant low- and high-range frequency components and a Frequency-Augmented Contrastive Learning (FACL) module to effectively separate domain-invariant mid-frequency features of clean and perturbed image. We demonstrate strong transferability of our generated adversarial perturbations through extensive cross-domain and cross-model experiments, while keeping the inference time complexity.<|reference_end|>
@article{yang2024facl-attack:,
    title={FACL-Attack: Frequency-Aware Contrastive Learning for Transferable
  Adversarial Attacks},
    author={Hunmin Yang, Jongoh Jeong, Kuk-Jin Yoon},
    journal={arXiv preprint arXiv:2407.20653},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20653},
    primaryClass={cs.CV cs.AI cs.LG}
}