Low-Rank and Sparse Decomposition for Low-Query Decision-Based Adversarial Attacks

Deep learning models are susceptible to contrived adversarial examples, even in the decision-based black-box setting where the attacker has access to the model's decisions only. Developing more efficient and practical attacks help in better understanding the limitations of deep models. It is important that attacks are crafted with limited queries to avoid suspicion. Since the required number of queries increase with dimensions, low-dimensional embeddings are attractive. This low query budget constraint is a bottleneck for learning-based and data-driven attacks which rely heavily on querying the model. We propose LSDAT, an image-agnostic non-data-driven decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) of images to dramatically reduce the queries and improve fooling rates compared to existing methods. LSDAT crafts perturbations in the low-dimensional subspace formed by the sparse component of the input image and that of a target adversarial image to obtain query-efficiency. A viable perturbation is obtained by traversing the path between the input and adversarial sparse components. Theoretical analyses are provided to justify the functionality of LSDAT. Unlike other competitors (e.g., FFT), LSD works directly in the image domain to guarantee that non- ℓ₂ constraints, such as sparsity, are satisfied. LSDAT offers better control over the number of queries and is computationally efficient as it performs sparse decomposition of the input and adversarial images only once to generate all queries. Four variants of LSDAT are presented for different scenarios including a pure black-box attack where no queries are allowed. We demonstrate ℓ₀, ℓ₂ and ℓ_∞ bounded attacks with LSDAT to evince its efficiency compared to baseline attacks in diverse low-query budget scenarios. LSDAT obtains 15 to 20% improvement in fooling ResNet-50 while using far fewer queries than competing methods in a similar setting.