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This work studies black-box adversarial attacks against deep neural networks(DNNs), where the attacker can only access the query feedback returned by theattacked DNN model, while other information such as model parameters or thetraining datasets are unknown. One promising approach to improve attackperformance is utilizing the adversarial transferability between some white-boxsurrogate models and the target model (i.e., the attacked model). However, dueto the possible differences on model architectures and training datasetsbetween surrogate and target models, dubbed "surrogate biases", thecontribution of adversarial transferability to improving the attack performancemay be weakened. To tackle this issue, we innovatively propose a black-boxattack method by developing a novel mechanism of adversarial transferability,which is robust to the surrogate biases. The general idea is transferringpartial parameters of the conditional adversarial distribution (CAD) ofsurrogate models, while learning the untransferred parameters based on queriesto the target model, to keep the flexibility to adjust the CAD of the targetmodel on any new benign sample. Extensive experiments on benchmark datasets andattacking against real-world API demonstrate the superior attack performance ofthe proposed method.

Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution