Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models

• URL: http://arxiv.org/abs/2202.00091v2
• Date: Fri, 24 Mar 2023 02:12:06 GMT
• Title: Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models
• Authors: Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe
• Abstract summary: We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. SparseEvo requires significantly fewer model queries than the state-of-the-art sparse attack Pointwise for both untargeted and targeted attacks. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general raise new questions regarding the safety of deployed systems.
• Score: 9.93052896330371
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS). Of particular interest are sparse attacks. The realization of sparse attacks in black-box models demonstrates that machine learning models are more vulnerable than we believe. Because these attacks aim to minimize the number of perturbed pixels measured by l_0 norm-required to mislead a model by solely observing the decision (the predicted label) returned to a model query; the so-called decision-based attack setting. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer model queries than the state-of-the-art sparse attack Pointwise for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is also competitive with only a limited query budget against the state-of-the-art gradient-based whitebox attacks in standard computer vision tasks such as ImageNet. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raise new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models.

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