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In this work, we present Phantom, a novel privacy-preserving framework for obfuscating deep neural network (DNN) model deployed in heterogeneous TEE/GPU systems. Phantom employs reinforcement learning to add lightweight obfuscation layers, degrading model performance for adversaries while maintaining functionality for authorized user. To reduce the off-chip data communication between TEE and GPU, we propose a Top-K layer-wise obfuscation sensitivity analysis method. Extensive experiments demonstrate Phantom's superiority over state-of-the-art (SoTA) defense methods against model stealing and fine-tuning attacks across various architectures and datasets. It reduces unauthorized accuracy to near-random guessing (e.g., 10% for CIFAR-10 tasks, 1% for CIFAR-100 tasks) and achieves a 6.99% average attack success rate for model stealing, significantly outperforming SoTA competing methods. System implementation on Intel SGX2 and NVIDIA GPU heterogeneous system achieves 35% end-to-end latency reduction compared with most recent SoTA work. 

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. 

Traditional Deep Neural Network (DNN) security is mostly related to the well-known adversarial input example attack.Recently, another dimension of adversarial attack, namely, attack on DNN weight parameters, has been shown to be very powerful. Asa representative one, the Bit-Flip based adversarial weight Attack (BFA) injects an extremely small amount of faults into weight parameters to hijack the executing DNN function. Prior works of BFA focus on un-targeted attacks that can hack all inputs into a random output class by flipping a very small number of weight bits stored in computer memory. This paper proposes the first work oftargetedBFA based (T-BFA) adversarial weight attack on DNNs, which can intentionally mislead selected inputs to a target output class. The objective is achieved by identifying the weight bits that are highly associated with classification of a targeted output through a class-dependent weight bit searching algorithm. Our proposed T-BFA performance is successfully demonstrated on multiple DNN architectures for image classification tasks. For example, by merely flipping 27 out of 88 million weight bits of ResNet-18, our T-BFA can misclassify all the images from Hen class into Goose class (i.e., 100% attack success rate) in ImageNet dataset, while maintaining 59.35% validation accuracy.