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Adversarial bit-flip attack (BFA), a type of powerful adversarial weight attack demonstrated in real computer systems has shown enormous success in compromising Deep Neural Network (DNN) performance with a minimal amount of model parameter perturbation through rowhammer-based computer main memory bit-flip. For the first time in this work, we demonstrate to defeat adversarial bit-flip attacks by developing a Robust and Accurate Binary Neural Network (RA-BNN) that adopts a complete BNN (i.e., weights and activations are both in binary). Prior works have demonstrated that binary or clustered weights could intrinsically improve a network's robustness against BFA, while in this work, we further reveal that binary activation could improve such robustness even better. However, with both aggressive binary weight and activation representations, the complete BNN suffers from poor clean (i.e., no attack) inference accuracy. To counter this, we propose an efficient two-stage complete BNN growing method for constructing simultaneously robust and accurate BNN, named RA-Growth. It selectively grows the channel size of each BNN layer based on trainable channel-wise binary mask learning with a Gumbel-Sigmoid function. The wider binary network (i.e., RA-BNN) has dual benefits: it can recover clean inference accuracy and significantly higher resistance against BFA. Our evaluation of the CIFAR-10 dataset shows that the proposed RA-BNN can improve the resistance to BFA by up to 100 x. On ImageNet, with a sufficiently large (e.g., 5,000) number of bit-flips, the baseline BNN accuracy drops to 4.3 % from 51.9 %, while our RA-BNN accuracy only drops to 37.1 % from 60.9 %, making it the best defense performance. 

FPGA virtualization has garnered significant industry and academic interests as it aims to enable multi-tenant cloud systems that can accommodate multiple users' circuits on a single FPGA. Although this approach greatly enhances the efficiency of hardware resource utilization, it also introduces new security concerns. As a representative study, one state-of-the-art (SOTA) adversarial fault injection attack, named Deep-Dup, exemplifies the vulnerabilities of off-chip data communication within the multi-tenant cloud-FPGA system. Deep-Dup attacks successfully demonstrate the complete failure of a wide range of Deep Neural Networks (DNNs) in a black-box setup, by only injecting fault to extremely small amounts of sensitive weight data transmissions, which are identified through a powerful differential evolution searching algorithm. Such emerging adversarial fault injection attack reveals the urgency of effective defense methodology to protect DNN applications on the multi-tenant cloud-FPGA system. This paper, for the first time, presents a novel moving-target-defense (MTD) oriented defense framework DeepShuffle, which could effectively protect DNNs on multi-tenant cloud-FPGA against the SOTA Deep-Dup attack, through a novel lightweight model parameter shuffling methodology. DeepShuffle effectively counters the Deep-Dup attack by altering the weight transmission sequence, which effectively prevents adversaries from identifying security-critical model parameters from the repeatability of weight transmission during each inference round. Importantly, DeepShuffle represents a training-free DNN defense methodology, which makes constructive use of the typologies of DNN architectures to achieve being lightweight. Moreover, the deployment of DeepShuffle neither requires any hardware modification nor suffers from any performance degradation. We evaluate DeepShuffle on the SOTA open-source FPGA-DNN accelerator, Vertical Tensor Accelerator (VTA), which represents the practice of real-world FPGA-DNN system developers. We then evaluate the performance overhead of DeepShuffle and find it only consumes an additional ~3% of the inference time compared to the unprotected baseline. DeepShuffle improves the robustness of various SOTA DNN architectures like VGG, ResNet, etc. against Deep-Dup by orders. It effectively reduces the efficacy of evolution searching-based adversarial fault injection attack close to random fault injection attack, e.g., on VGG-11, even after increasing the attacker's effort by 2.3x, our defense shows a ~93% improvement in accuracy, compared to the unprotected baseline. 

Transfer learning, where the goal is to transfer the well-trained deep learning models from a primary source task to a new task, is a crucial learning scheme for on-device machine learning, due to the fact that IoT/edge devices collect and then process massive data in our daily life. However, due to the tiny memory constraint in IoT/edge devices, such on-device learning requires ultra-small training memory footprint, bringing new challenges for memory-efficient learning. Many existing works solve this problem by reducing the number of trainable parameters. However, this doesn't directly translate to memory-saving since the major bottleneck is the activations, not parameters. To develop memory-efficient on-device transfer learning, in this work, we are the first to approach the concept of transfer learning from a new perspective of intermediate feature reprogramming of a pre-trained model (i.e., backbone). To perform this lightweight and memory-efficient reprogramming, we propose to train a tiny Reprogramming Network (Rep-Net) directly from the new task input data, while freezing the backbone model. The proposed Rep-Net model interchanges the features with the backbone model using an activation connector at regular intervals to mutually benefit both the backbone model and Rep-Net model features. Through extensive experiments, we validate each design specs of the proposed Rep-Net model in achieving highly memory-efficient on-device reprogramming. Our experiments establish the superior performance (i.e., low training memory and high accuracy) of Rep-Net compared to SOTA on-device transfer learning schemes across multiple benchmarks. 

Recent advancements in Deep Neural Networks (DNNs) have enabled widespread deployment in multiple security-sensitive domains. The need for resource-intensive training and the use of valuable domain-specific training data have made these models the top intellectual property (IP) for model owners. One of the major threats to DNN privacy is model extraction attacks where adversaries attempt to steal sensitive information in DNN models. In this work, we propose an advanced model extraction framework DeepSteal that steals DNN weights remotely for the first time with the aid of a memory side-channel attack. Our proposed DeepSteal comprises two key stages. Firstly, we develop a new weight bit information extraction method, called HammerLeak, through adopting the rowhammer-based fault technique as the information leakage vector. HammerLeak leverages several novel system-level techniques tailored for DNN applications to enable fast and efficient weight stealing. Secondly, we propose a novel substitute model training algorithm with Mean Clustering weight penalty, which leverages the partial leaked bit information effectively and generates a substitute prototype of the target victim model. We evaluate the proposed model extraction framework on three popular image datasets (e.g., CIFAR-10/100/GTSRB) and four DNN architectures (e.g., ResNet-18/34/Wide-ResNetNGG-11). The extracted substitute model has successfully achieved more than 90% test accuracy on deep residual networks for the CIFAR-10 dataset. Moreover, our extracted substitute model could also generate effective adversarial input samples to fool the victim model. Notably, it achieves similar performance (i.e., ~1-2% test accuracy under attack) as white-box adversarial input attack (e.g., PGD/Trades).