Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition
This work addresses a practical security threat for action recognition systems by making black-box attacks more efficient and transferable, though it is incremental as it builds on existing attack paradigms.
The paper tackles the problem of query inefficiency and data dependency in black-box adversarial attacks on action recognition systems by generating perturbations to disrupt features from a substitute model trained on a nearly disjoint dataset, achieving 8% and 12% higher deception rates compared to state-of-the-art methods.
Black-box adversarial attacks present a realistic threat to action recognition systems. Existing black-box attacks follow either a query-based approach where an attack is optimized by querying the target model, or a transfer-based approach where attacks are generated using a substitute model. While these methods can achieve decent fooling rates, the former tends to be highly query-inefficient while the latter assumes extensive knowledge of the black-box model's training data. In this paper, we propose a new attack on action recognition that addresses these shortcomings by generating perturbations to disrupt the features learned by a pre-trained substitute model to reduce the number of queries. By using a nearly disjoint dataset to train the substitute model, our method removes the requirement that the substitute model be trained using the same dataset as the target model, and leverages queries to the target model to retain the fooling rate benefits provided by query-based methods. This ultimately results in attacks which are more transferable than conventional black-box attacks. Through extensive experiments, we demonstrate highly query-efficient black-box attacks with the proposed framework. Our method achieves 8% and 12% higher deception rates compared to state-of-the-art query-based and transfer-based attacks, respectively.