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Register a new userOn the Intrinsic Robustness of NVM Crossbars Against Adversarial Attacks
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Deboleena Roy
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ Matrix Vector Multiplication (MVM) thus alleviating the long-standing von Neuman bottleneck in todays digital hardware. However, the analog nature of computing in these crossbars is inherently approximate and results in deviations from ideal output values, which reduces the overall performance of Deep Neural Networks (DNNs) under normal circumstances. In this paper, we study the impact of these non-idealities under adversarial circumstances. We show that the non-ideal behavior of analog computing lowers the effectiveness of adversarial attacks, in both Black-Box and White-Box attack scenarios. In a non-adaptive attack, where the attacker is unaware of the analog hardware, we observe that analog computing offers a varying degree of intrinsic robustness, with a peak adversarial accuracy improvement of 35.34%, 22.69%, and 9.90% for white box PGD (epsilon=1/255, iter=30) for CIFAR-10, CIFAR-100, and ImageNet respectively. We also demonstrate Hardware-in-Loop adaptive attacks that circumvent this robustness by utilizing the knowledge of the NVM model.
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Attention-based networks have achieved state-of-the-art performance in many computer vision tasks, such as image classification. Unlike Convolutional Neural Network (CNN), the major part of the vanilla Vision Transformer (ViT) is the attention block that brings the power of mimicking the global context of the input image. This power is data hunger and hence, the larger the training data the better the performance. To overcome this limitation, many ViT-based networks, or hybrid-ViT, have been proposed to include local context during the training. The robustness of ViTs and its variants against adversarial attacks has not been widely invested in the literature. Some robustness attributes were revealed in few previous works and hence, more insight robustness attributes are yet unrevealed. This work studies the robustness of ViT variants 1) against different $L_p$-based adversarial attacks in comparison with CNNs and 2) under Adversarial Examples (AEs) after applying preprocessing defense methods. To that end, we run a set of experiments on 1000 images from ImageNet-1k and then provide an analysis that reveals that vanilla ViT or hybrid-ViT are more robust than CNNs. For instance, we found that 1) Vanilla ViTs or hybrid-ViTs are more robust than CNNs under $L_0$, $L_1$, $L_2$, $L_infty$-based, and Color Channel Perturbations (CCP) attacks. 2) Vanilla ViTs are not responding to preprocessing defenses that mainly reduce the high frequency components while, hybrid-ViTs are more responsive to such defense. 3) CCP can be used as a preprocessing defense and larger ViT variants are found to be more responsive than other models. Furthermore, feature maps, attention maps, and Grad-CAM visualization jointly with image quality measures, and perturbations energy spectrum are provided for an insight understanding of attention-based models.
Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the l_{2} norm is useful for provable adversarial robustness, interpretable gradients and stable training. While 1-Lipschitz CNNs can be designed by enforcing a 1-Lipschitz constraint on each layer, training such networks requires each layer to have an orthogonal Jacobian matrix (for all inputs) to prevent gradients from vanishing during backpropagation. A layer with this property is said to be Gradient Norm Preserving (GNP). To construct expressive GNP activation functions, we first prove that the Jacobian of any GNP piecewise linear function is only allowed to change via Householder transformations for the function to be continuous. Building on this result, we introduce a class of nonlinear GNP activations with learnable Householder transformations called Householder activations. A householder activation parameterized by the vector $mathbf{v}$ outputs $(mathbf{I} - 2mathbf{v}mathbf{v}^{T})mathbf{z}$ for its input $mathbf{z}$ if $mathbf{v}^{T}mathbf{z} leq 0$; otherwise it outputs $mathbf{z}$. Existing GNP activations such as $mathrm{MaxMin}$ can be viewed as special cases of $mathrm{HH}$ activations for certain settings of these transformations. Thus, networks with $mathrm{HH}$ activations have higher expressive power than those with $mathrm{MaxMin}$ activations. Although networks with $mathrm{HH}$ activations have nontrivial provable robustness against adversarial attacks, we further boost their robustness by (i) introducing a certificate regularization and (ii) relaxing orthogonalization of the last layer of the network. Our experiments on CIFAR-10 and CIFAR-100 show that our regularized networks with $mathrm{HH}$ activations lead to significant improvements in both the standard and provable robust accuracy over the prior works (gain of 3.65% and 4.46% on CIFAR-100 respectively).
We consider a wireless communication system that consists of a background emitter, a transmitter, and an adversary. The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle. Concurrently, the adversary trains its own DNN classifier as the surrogate model by observing the spectrum to detect the ongoing transmissions of the background emitter and generate adversarial attacks to fool the transmitter into misclassifying the channel as idle. This surrogate model may differ from the transmitters classifier significantly because the adversary and the transmitter experience different channels from the background emitter and therefore their classifiers are trained with different distributions of inputs. This system model may represent a setting where the background emitter is a primary user, the transmitter is a secondary user, and the adversary is trying to fool the secondary user to transmit even though the channel is occupied by the primary user. We consider different topologies to investigate how different surrogate models that are trained by the adversary (depending on the differences in channel effects experienced by the adversary) affect the performance of the adversarial attack. The simulation results show that the surrogate models that are trained with different distributions of channel-induced inputs severely limit the attack performance and indicate that the transferability of adversarial attacks is neither readily available nor straightforward to achieve since surrogate models for wireless applications may significantly differ from the target model depending on channel effects.
State-of-the-art deep neural networks (DNNs) have been proved to have excellent performance on unsupervised domain adaption (UDA). However, recent work shows that DNNs perform poorly when being attacked by adversarial samples, where these attacks are implemented by simply adding small disturbances to the original images. Although plenty of work has focused on this, as far as we know, there is no systematic research on the robustness of unsupervised domain adaption model. Hence, we discuss the robustness of unsupervised domain adaption against adversarial attacking for the first time. We benchmark various settings of adversarial attack and defense in domain adaption, and propose a cross domain attack method based on pseudo label. Most importantly, we analyze the impact of different datasets, models, attack methods and defense methods. Directly, our work proves the limited robustness of unsupervised domain adaptation model, and we hope our work may facilitate the community to pay more attention to improve the robustness of the model against attacking.
Recent studies have shown that deep neural networks (DNNs) are highly vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive interests in both empirical and provable robustness against evasion attacks; however, provable robustness against backdoor attacks remains largely unexplored. In this paper, we focus on certifying robustness against backdoor attacks. To this end, we first provide a unified framework for robustness certification and show that it leads to a tight robustness condition for backdoor attacks. We then propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. Moreover, we evaluate the certified robustness of a family of smoothed models which are trained in a differentially private fashion, and show that they achieve better certified robustness bounds. In addition, we theoretically show that it is possible to train the robust smoothed models efficiently for simple models such as K-nearest neighbor classifiers, and we propose an exact smooth-training algorithm which eliminates the need to sample from a noise distribution. Empirically, we conduct comprehensive experiments for different machine learning (ML) models such as DNNs, differentially private DNNs, and K-NN models on MNIST, CIFAR-10 and ImageNet datasets (focusing on binary classifiers), and provide the first benchmark for certified robustness against backdoor attacks. In addition, we evaluate K-NN models on a spambase tabular dataset to demonstrate the advantages of the proposed exact algorithm. Both the theoretical analysis and the comprehensive benchmark on diverse ML models and datasets shed lights on further robust learning strategies against training time attacks or other general adversarial attacks.
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