Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions

• URL: http://arxiv.org/abs/2212.13707v1
• Date: Wed, 28 Dec 2022 05:47:19 GMT
• Title: Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions
• Authors: Kamilya Smagulova, Mohammed E. Fouda and Ahmed Eltawil
• Abstract summary: This paper studied robustness of new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.
• Score: 0.5672132510411465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Increasing popularity of deep-learning-powered applications raises the issue of vulnerability of neural networks to adversarial attacks. In other words, hardly perceptible changes in input data lead to the output error in neural network hindering their utilization in applications that involve decisions with security risks. A number of previous works have already thoroughly evaluated the most commonly used configuration - Convolutional Neural Networks (CNNs) against different types of adversarial attacks. Moreover, recent works demonstrated transferability of the some adversarial examples across different neural network models. This paper studied robustness of the new emerging models such as SpinalNet-based neural networks and Compact Convolutional Transformers (CCT) on image classification problem of CIFAR-10 dataset. Each architecture was tested against four White-box attacks and three Black-box attacks. Unlike VGG and SpinalNet models, attention-based CCT configuration demonstrated large span between strong robustness and vulnerability to adversarial examples. Eventually, the study of transferability between VGG, VGG-inspired SpinalNet and pretrained CCT 7/3x1 models was conducted. It was shown that despite high effectiveness of the attack on the certain individual model, this does not guarantee the transferability to other models.

Related papers

• Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection [22.99930028876662]
  Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. Current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system. We propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks.
  arXiv  Detail & Related papers  (2022-12-13T17:51:32Z)
• Dynamics-aware Adversarial Attack of Adaptive Neural Networks [75.50214601278455]
  We investigate the dynamics-aware adversarial attack problem of adaptive neural networks. We propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. Our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods.
  arXiv  Detail & Related papers  (2022-10-15T01:32:08Z)
• Learning Robust Kernel Ensembles with Kernel Average Pooling [3.6540368812166872]
  We introduce Kernel Average Pooling (KAP), a neural network building block that applies the mean filter along the kernel dimension of the layer activation tensor. We show that ensembles of kernels with similar functionality naturally emerge in convolutional neural networks equipped with KAP and trained with backpropagation.
  arXiv  Detail & Related papers  (2022-09-30T19:49:14Z)
• From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks [82.21746840893658]
  This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. We show that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary.
  arXiv  Detail & Related papers  (2022-04-14T15:14:08Z)
• Efficient and Robust Classification for Sparse Attacks [34.48667992227529]
  We consider perturbations bounded by the $ell$--norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. We propose a novel defense method that consists of "truncation" and "adrial training" Motivated by the insights we obtain, we extend these components to neural network classifiers.
  arXiv  Detail & Related papers  (2022-01-23T21:18:17Z)
• Pruning in the Face of Adversaries [0.0]
  We evaluate the impact of neural network pruning on the adversarial robustness against L-0, L-2 and L-infinity attacks. Our results confirm that neural network pruning and adversarial robustness are not mutually exclusive. We extend our analysis to situations that incorporate additional assumptions on the adversarial scenario and show that depending on the situation, different strategies are optimal.
  arXiv  Detail & Related papers  (2021-08-19T09:06:16Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• BreakingBED -- Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks [65.2021953284622]
  We study robustness of CNNs against white-box and black-box adversarial attacks. Results are shown for distilled CNNs, agent-based state-of-the-art pruned models, and binarized neural networks.
  arXiv  Detail & Related papers  (2021-03-14T20:43:19Z)
• Improving Neural Network Robustness through Neighborhood Preserving Layers [0.751016548830037]
  We demonstrate a novel neural network architecture which can incorporate such layers and also can be trained efficiently. We empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks.
  arXiv  Detail & Related papers  (2021-01-28T01:26:35Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness [97.67477497115163]
  We use mode connectivity to study the adversarial robustness of deep neural networks. Our experiments cover various types of adversarial attacks applied to different network architectures and datasets. Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness.
  arXiv  Detail & Related papers  (2020-04-30T19:12:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.