Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks

• URL: http://arxiv.org/abs/2101.01543v1
• Date: Tue, 5 Jan 2021 14:31:53 GMT
• Title: Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks
• Authors: Rachel Sterneck, Abhishek Moitra, Priyadarshini Panda
• Abstract summary: Adversarial examples are inputs that have been carefully perturbed to fool classifier networks, while appearing unchanged to humans. We propose a structured methodology of augmenting a deep neural network (DNN) with a detector subnetwork. We show that our method improves state-of-the-art detector robustness against adversarial examples.
• Score: 3.125321230840342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks have achieved remarkable performance in computer vision, however they are vulnerable to adversarial examples. Adversarial examples are inputs that have been carefully perturbed to fool classifier networks, while appearing unchanged to humans. Based on prior works on detecting adversaries, we propose a structured methodology of augmenting a deep neural network (DNN) with a detector subnetwork. We use $\textit{Adversarial Noise Sensitivity}$ (ANS), a novel metric for measuring the adversarial gradient contribution of different intermediate layers of a network. Based on the ANS value, we append a detector to the most sensitive layer. In prior works, more complex detectors were added to a DNN, increasing the inference computational cost of the model. In contrast, our structured and strategic addition of a detector to a DNN reduces the complexity of the model while making the overall network adversarially resilient. Through comprehensive white-box and black-box experiments on MNIST, CIFAR-10, and CIFAR-100, we show that our method improves state-of-the-art detector robustness against adversarial examples. Furthermore, we validate the energy efficiency of our proposed adversarial detection methodology through an extensive energy analysis on various hardware scalable CMOS accelerator platforms. We also demonstrate the effects of quantization on our detector-appended networks.

Related papers

• Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection [0.0]
  We propose an Enhanced Convolutional Neural Network (EnCNN) for Network Intrusion Detection Systems (NIDS) We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble. The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches.
  arXiv  Detail & Related papers  (2024-09-27T11:20:20Z)
• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• A Geometrical Approach to Evaluate the Adversarial Robustness of Deep Neural Networks [52.09243852066406]
  Adversarial Converging Time Score (ACTS) measures the converging time as an adversarial robustness metric. We validate the effectiveness and generalization of the proposed ACTS metric against different adversarial attacks on the large-scale ImageNet dataset.
  arXiv  Detail & Related papers  (2023-10-10T09:39:38Z)
• 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)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• On the Robustness and Anomaly Detection of Sparse Neural Networks [28.832060124537843]
  We show that sparsity can make networks more robust and better anomaly detectors. We also show that structured sparsity greatly helps in reducing the complexity of expensive robustness and detection methods. We introduce a new method, SensNorm, which uses the sensitivity of weights derived from an appropriate pruning method to detect anomalous samples.
  arXiv  Detail & Related papers  (2022-07-09T09:03:52Z)
• SAR Despeckling Using Overcomplete Convolutional Networks [53.99620005035804]
  despeckling is an important problem in remote sensing as speckle degrades SAR images. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
  arXiv  Detail & Related papers  (2022-05-31T15:55:37Z)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• An Empirical Study of Adder Neural Networks for Object Detection [67.64041181937624]
  Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations. We present an empirical study of AdderNets for object detection.
  arXiv  Detail & Related papers  (2021-12-27T11:03:13Z)
• 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)
• QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks [3.2242513084255036]
  QUANOS is a framework that performs layer-specific hybrid quantization based on Adversarial Noise Sensitivity (ANS) Our experiments on CIFAR10, CIFAR100 datasets show that QUANOS outperforms homogenously quantized 8-bit precision baseline in terms of adversarial robustness.
  arXiv  Detail & Related papers  (2020-04-22T15:56:31Z)

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.