Basura Fernando

Corrupting Attention: Evasion-Based Adversarial Attacks on Encoder Attention in Detection Transformers

This paper introduces the first attack that directly optimizes an encoder-attention objective under an imperceptible, bounded, bounded perturbation, and argues that encoder attention concentrates the model's spatial reasoning, so corrupting it propagates through the detection pipeline more disruptively than perturbing the detection output alone.