Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation
This work addresses a critical security issue in machine learning by proposing a novel defense against transferable adversarial examples, though it appears incremental in the context of existing adversarial robustness research.
The paper tackles the problem of transferable adversarial examples in deep neural networks by introducing a model that behaves randomly on clean data but predicts accurately when a constant trigger is added, achieving improved robustness against such attacks as demonstrated through extensive experiments.
Adversarial examples, characterized by imperceptible perturbations, pose significant threats to deep neural networks by misleading their predictions. A critical aspect of these examples is their transferability, allowing them to deceive {unseen} models in black-box scenarios. Despite the widespread exploration of defense methods, including those on transferability, they show limitations: inefficient deployment, ineffective defense, and degraded performance on clean images. In this work, we introduce a novel training paradigm aimed at enhancing robustness against transferable adversarial examples (TAEs) in a more efficient and effective way. We propose a model that exhibits random guessing behavior when presented with clean data $\boldsymbol{x}$ as input, and generates accurate predictions when with triggered data $\boldsymbol{x}+\boldsymbolτ$. Importantly, the trigger $\boldsymbolτ$ remains constant for all data instances. We refer to these models as \textbf{models with trigger activation}. We are surprised to find that these models exhibit certain robustness against TAEs. Through the consideration of first-order gradients, we provide a theoretical analysis of this robustness. Moreover, through the joint optimization of the learnable trigger and the model, we achieve improved robustness to transferable attacks. Extensive experiments conducted across diverse datasets, evaluating a variety of attacking methods, underscore the effectiveness and superiority of our approach.