Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks

• URL: http://arxiv.org/abs/2107.09804v1
• Date: Tue, 20 Jul 2021 23:21:04 GMT
• Title: Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks
• Authors: Saikat Majumdar, Mohammad Hossein Samavatian, Kristin Barber, Radu Teodorescu
• Abstract summary: We propose a novel, lightweight adversarial correction and/or detection mechanism for image classifiers. We show that these errors disrupt the adversarial input in a way that can be used either to correct the classification or detect the input as adversarial.
• Score: 1.9212368803706579
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural network (DNN) classifiers are powerful tools that drive a broad spectrum of important applications, from image recognition to autonomous vehicles. Unfortunately, DNNs are known to be vulnerable to adversarial attacks that affect virtually all state-of-the-art models. These attacks make small imperceptible modifications to inputs that are sufficient to induce the DNNs to produce the wrong classification. In this paper we propose a novel, lightweight adversarial correction and/or detection mechanism for image classifiers that relies on undervolting (running a chip at a voltage that is slightly below its safe margin). We propose using controlled undervolting of the chip running the inference process in order to introduce a limited number of compute errors. We show that these errors disrupt the adversarial input in a way that can be used either to correct the classification or detect the input as adversarial. We evaluate the proposed solution in an FPGA design and through software simulation. We evaluate 10 attacks on two popular DNNs and show an average detection rate of 80% to 95%.

Related papers

• DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning [2.485182034310304]
  We propose a hardware-accelerated defense against machine learning attacks. DNNSHIELD adapts the strength of the response to the confidence of the adversarial input. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50.
  arXiv  Detail & Related papers  (2022-07-31T19:29:44Z)
• A Mask-Based Adversarial Defense Scheme [3.759725391906588]
  Adversarial attacks hamper the functionality and accuracy of Deep Neural Networks (DNNs) We propose a new Mask-based Adversarial Defense scheme (MAD) for DNNs to mitigate the negative effect from adversarial attacks.
  arXiv  Detail & Related papers  (2022-04-21T12:55:27Z)
• AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks [18.461079157949698]
  backdoor poisoning attacks against image classification networks. We propose lightweight automated detection and correction techniques against poisoning attacks. Our technique outperforms existing defenses such as NeuralCleanse and STRIP on popular benchmarks.
  arXiv  Detail & Related papers  (2022-01-31T23:42:32Z)
• Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting [61.99564267735242]
  Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems. Recent studies have demonstrated that deep neural network (DNN) methods are vulnerable to adversarial attacks. We propose a robust attack strategy called Adversarial Patch Attack with Momentum to evaluate the robustness of crowd counting models.
  arXiv  Detail & Related papers  (2021-04-22T05:10:55Z)
• 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)
• Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits [55.740716446995805]
  We study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Our goal is to misclassify a specific sample into a target class without any sample modification. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem.
  arXiv  Detail & Related papers  (2021-02-21T03:13:27Z)
• Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids [7.351477761427584]
  False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. Recent studies have proposed machine learning (ML) techniques, particularly deep neural networks (DNNs)
  arXiv  Detail & Related papers  (2021-02-17T22:26:34Z)
• Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network [62.77129284830945]
  We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
  arXiv  Detail & Related papers  (2021-01-28T16:18:19Z)
• GreedyFool: Distortion-Aware Sparse Adversarial Attack [138.55076781355206]
  Modern deep neural networks (DNNs) are vulnerable to adversarial samples. Sparse adversarial samples can fool the target model by only perturbing a few pixels. We propose a novel two-stage distortion-aware greedy-based method dubbed as "GreedyFool"
  arXiv  Detail & Related papers  (2020-10-26T17:59:07Z)
• Detection of Iterative Adversarial Attacks via Counter Attack [4.549831511476249]
  Deep neural networks (DNNs) have proven to be powerful tools for processing unstructured data. For high-dimensional data, like images, they are inherently vulnerable to adversarial attacks. In this work we outline a mathematical proof that the CW attack can be used as a detector itself.
  arXiv  Detail & Related papers  (2020-09-23T21:54:36Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)

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.