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1. Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA

   Rakin, Adnan Siraj; Luo, Yukui; Xu, Xiaolin; Fan, Deliang (August 2021, 30th USENIX Security Symposium)

   The wide deployment of Deep Neural Networks (DNN) in high-performance cloud computing platforms brought to light multi-tenant cloud field-programmable gate arrays (FPGA) as a popular choice of accelerator to boost performance due to its hardware reprogramming flexibility. Such a multi-tenant FPGA setup for DNN acceleration potentially exposes DNN interference tasks under severe threat from malicious users. This work, to the best of our knowledge, is the first to explore DNN model vulnerabilities in multi-tenant FPGAs. We propose a novel adversarial attack framework: Deep-Dup, in which the adversarial tenant can inject adversarial faults to the DNN model in the victim tenant of FPGA. Specifically, she can aggressively overload the shared power distribution system of FPGA with malicious power-plundering circuits, achieving adversarial weight duplication (AWD) hardware attack that duplicates certain DNN weight packages during data transmission between off-chip memory and on-chip buffer, to hijack the DNN function of the victim tenant. Further, to identify the most vulnerable DNN weight packages for a given malicious objective, we propose a generic vulnerable weight package searching algorithm, called Progressive Differential Evolution Search (P-DES), which is, for the first time, adaptive to both deep learning white-box and black-box attack models. The proposed Deep-Dup is experimentally validated in a developed multi-tenant FPGA prototype, for two popular deep learning applications, i.e., Object Detection and Image Classification. Successful attacks are demonstrated in six popular DNN architectures (e.g., YOLOv2, ResNet-50, MobileNet, etc.) on three datasets (COCO, CIFAR-10, and ImageNet). 

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2. Adversarial Autoencoder based Model Extraction Attacks for Collaborative DNN Inference at Edge

   https://doi.org/10.1109/NOMS57970.2025.11073724  

   Zneit, Manal; Zhang, Xiaojie; Mounesan, Motahare; Debroy, Saptarshi (May 2025, IEEE)

   Deep neural networks (DNNs) are influencing a wide range of applications from safety-critical to security-sensitive use cases. In many such use cases, the DNN inference process relies on distributed systems involving IoT devices and edge/cloud severs as participants where a pre-trained DNN model is partitioned/split onto multiple parts and the participants collaboratively execute them. However, often such collaboration requires dynamic DNN partitioning information to be exchanged among the participants over unsecured network or via relays/hops which can lead to novel privacy vulnerabilities. In this paper, we propose a DNN model extraction attack that exploits such vulnerabilities to not only extract the original input data, but also reconstruct the entire victim DNN model. Specifically, the proposed attack model utilizes extracted/leaked data and adversarial autoencoders to generate and train a shadow model that closely mimics the behavior of the original victim model. The proposed attack is query-free and does not require the attacker to have any prior information about the victim model and input data. Using an IoT- edge hardware testbed running collaborative DNN inference, we demonstrate the effectiveness of the proposed attack model in extracting the victim model with high levels of certainty across many realistic scenarios. 

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3. Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack

   https://doi.org/10.1109/CVPR42600.2020.01410  

   He, Zhezhi; Rakin, Adnan Siraj; Li, Jingtao; Chakrabarti, Chaitali; Fan, Deliang (June 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   null (Ed.)

   Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit-Flip Attack (BFA). BFA has shown extraordinary attacking ability, where the adversary can malfunction a quantized Deep Neural Network (DNN) as a random guess, through malicious bit-flips on a small set of vulnerable weight bits (e.g., 13 out of 93 millions bits of 8-bit quantized ResNet-18). However, there are no effective defensive methods to enhance the fault-tolerance capability of DNN against such BFA. In this work, we conduct comprehensive investigations on BFA and propose to leverage binarization-aware training and its relaxation - piece-wise clustering as simple and effective countermeasures to BFA. The experiments show that, for BFA to achieve the identical prediction accuracy degradation (e.g., below 11% on CIFAR-10), it requires 19.3× and 480.1× more effective malicious bit-flips on ResNet-20 and VGG-11 respectively, compared to defend-free counterparts. 

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4. Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks

   https://doi.org/10.1088/1361-6641/ac461f  

   Kiran Cherupally, Sai; Meng, Jian; Siraj Rakin, Adnan; Yin, Shihui; Yeo, Injune; Yu, Shimeng; Fan, Deliang; Seo, Jae-Sun (January 2022, Semiconductor Science and Technology)

   Abstract We present a novel deep neural network (DNN) training scheme and resistive RAM (RRAM) in-memory computing (IMC) hardware evaluation towards achieving high accuracy against RRAM device/array variations and enhanced robustness against adversarial input attacks. We present improved IMC inference accuracy results evaluated on state-of-the-art DNNs including ResNet-18, AlexNet, and VGG with binary, 2-bit, and 4-bit activation/weight precision for the CIFAR-10 dataset. These DNNs are evaluated with measured noise data obtained from three different RRAM-based IMC prototype chips. Across these various DNNs and IMC chip measurements, we show that our proposed hardware noise-aware DNN training consistently improves DNN inference accuracy for actual IMC hardware, up to 8% accuracy improvement for the CIFAR-10 dataset. We also analyze the impact of our proposed noise injection scheme on the adversarial robustness of ResNet-18 DNNs with 1-bit, 2-bit, and 4-bit activation/weight precision. Our results show up to 6% improvement in the robustness to black-box adversarial input attacks. 

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5. Robust Sparse Regularization: Defending Adversarial Attacks Via Regularized Sparse Network

   https://doi.org/10.1145/3386263.3407651  

   Rakin, Adnan Siraj; He, Zhezhi; Yang, Li; Wang, Yanzhi; Wang, Liqiang; Fan, Deliang (September 2020, Proceedings of the 2020 on Great Lakes Symposium on VLSI)

   null (Ed.)

   Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the countermeasures against adversarial attacks, increasing the model capacity for DNN robustness enhancement was discussed and reported as an effective approach by many recent works. In this work, we show that shrinking the model size through proper weight pruning can even be helpful to improve the DNN robustness under adversarial attack. For obtaining a simultaneously robust and compact DNN model, we propose a multi-objective training method called Robust Sparse Regularization (RSR), through the fusion of various regularization techniques, including channel-wise noise injection, lasso weight penalty, and adversarial training. We conduct extensive experiments to show the effectiveness of RSR against popular white-box (i.e., PGD and FGSM) and black-box attacks. Thanks to RSR, 85 % weight connections of ResNet-18 can be pruned while still achieving 0.68 % and 8.72 % improvement in clean- and perturbed-data accuracy respectively on CIFAR-10 dataset, in comparison to its PGD adversarial training baseline. 

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