Transformation-Dependent Adversarial Attacks
<|reference_start|>Transformation-Dependent Adversarial Attacks: We introduce transformation-dependent adversarial attacks, a new class of threats where a single additive perturbation can trigger diverse, controllable mis-predictions by systematically transforming the input (e.g., scaling, blurring, compression). Unlike traditional attacks with static effects, our perturbations embed metamorphic properties to enable different adversarial attacks as a function of the transformation parameters. We demonstrate the transformation-dependent vulnerability across models (e.g., convolutional networks and vision transformers) and vision tasks (e.g., image classification and object detection). Our proposed geometric and photometric transformations enable a range of targeted errors from one crafted input (e.g., higher than 90% attack success rate for classifiers). We analyze effects of model architecture and type/variety of transformations on attack effectiveness. This work forces a paradigm shift by redefining adversarial inputs as dynamic, controllable threats. We highlight the need for robust defenses against such multifaceted, chameleon-like perturbations that current techniques are ill-prepared for.<|reference_end|>
@article{tan2024transformation-dependent,
    title={Transformation-Dependent Adversarial Attacks},
    author={Yaoteng Tan, Zikui Cai, and M. Salman Asif},
    journal={arXiv preprint arXiv:2406.08443},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08443},
    primaryClass={cs.CV cs.LG}
}