Transformation-Dependent Adversarial Attacks

• URL: http://arxiv.org/abs/2406.08443v1
• Date: Wed, 12 Jun 2024 17:31:36 GMT
• Title: Transformation-Dependent Adversarial Attacks
• Authors: Yaoteng Tan, Zikui Cai, M. Salman Asif,
• Abstract summary: We introduce transformation-dependent adversarial attacks, a new class of threats where a single additive perturbation can trigger diverse, controllable mis-predictions. Unlike traditional attacks with static effects, our perturbations embed metamorphic properties to enable different adversarial attacks as a function of the transformation parameters.
• Score: 15.374381635334897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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