Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
This work addresses the challenge of creating more robust and efficient adversarial attacks for deep learning models, which is important for security and robustness testing, though it is incremental as it builds on existing meta-learning methods.
The paper tackles the problem of generating universal adversarial perturbations (UAPs) that work across different image sources, such as varying resolutions, by framing it as a few-shot learning task using bilevel optimization and learning-to-optimize techniques, resulting in a 50% higher attack success rate and 37% faster performance compared to baselines.
Adversarial perturbations are critical for certifying the robustness of deep learning models. A universal adversarial perturbation (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack algorithm. However, the existing UAP generator is underdeveloped when images are drawn from different image sources (e.g., with different image resolutions). Towards an authentic universality across image sources, we take a novel view of UAP generation as a customized instance of few-shot learning, which leverages bilevel optimization and learning-to-optimize (L2O) techniques for UAP generation with improved attack success rate (ASR). We begin by considering the popular model agnostic meta-learning (MAML) framework to meta-learn a UAP generator. However, we see that the MAML framework does not directly offer the universal attack across image sources, requiring us to integrate it with another meta-learning framework of L2O. The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and image data sources.