Entropy-Based Modeling for Estimating Adversarial Bit-flip Attack Impact on Binarized Neural Network
Date of Original Version
Over past years, the high demand to efficiently process deep learning (DL) models has driven the market of the chip design companies. However, the new Deep Chip architectures, a common term to refer to DL hardware accelerator, have slightly paid attention to the security requirements in quantized neural networks (QNNs), while the black/white -box adversarial attacks can jeopardize the integrity of the inference accelerator. Therefore in this paper, a comprehensive study of the resiliency of QNN topologies to black-box attacks is examined. Herein, different attack scenarios are performed on an FPGA-processor co-design, and the collected results are extensively analyzed to give an estimation of the impact's degree of different types of attacks on the QNN topology. To be specific, we evaluated the sensitivity of the QNN accelerator to a range number of bitflip attacks (BFAs) that might occur in the operational lifetime of the device. The BFAs are injected at uniformly distributed times either across the entire QNN or per individual layer during the image classification. The acquired results are utilized to build the entropy-based model that can be leveraged to construct resilient QNN architectures to bit-flip attacks.
Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Khoshavi, Navid, Saman Sargolzaei, Yu Bi, and Arman Roohi. "Entropy-Based Modeling for Estimating Adversarial Bit-flip Attack Impact on Binarized Neural Network." Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC , (2021): 493-498. doi:10.1145/3394885.3431594.