IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networks
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Abstract (arXiv)
The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples, the legitimate input added with imperceptible and well-designed perturbations that can fool DNNs easily in the DNN testing/deploying stage. Previous adversarial example generation algorithms for adversarial white-box attacks used Jacobian gradient information to add perturbations. This information is too imprecise and inexplicit, which will cause unnecessary perturbations when generating adversarial examples. This paper aims to address this issue. We first propose to apply a more informative and distilled gradient information, namely integrated gradient, to generate adversarial examples. To further make the perturbations more imperceptible, we propose to employ the restriction combination of L0 and L1/L2 secondly, which can restrict the total perturbations and perturbation points simultaneously. Meanwhile, to address the non-differentiable problem of L1, we explore a proximal operation of L1 thirdly. Based on these three works, we propose two Integrated gradient based White-box Adversarial example generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset in order to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured datasets, and we compare them with five baseline generation algorithms. The results show that our proposed algorithms do craft adversarial examples with more imperceptible perturbations and satisfactory crafting rate. L2 restriction is more suitable for unstructured dataset and L1 restriction performs better in structured dataset.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 46 references that are in the map. First posted on arXiv 3 Feb 2021.
Builds on
31 of 46 references.
References · 31
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
- 2013Intriguing properties of neural networkscited by 5,114
- 2017Axiomatic Attribution for Deep Networkscited by 3,350
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
- 2016Adversarial examples in the physical worldcited by 1,762
- 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
- 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,442
- 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
19 more
- 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
- 2016An overview of gradient descent optimization algorithmscited by 1,021
- 2016Adversarial Machine Learning at Scalecited by 934
- 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
- 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
- 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 656
- 2017One pixel attack for fooling deep neural networkscited by 579
- 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
- 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
- 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 350
- 2019Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attackcited by 333
- 2019Lookahead Optimizer: k steps forward, 1 step backcited by 333
- 2020AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradientscited by 282
- 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
- 2019NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networkscited by 94
- 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 74
- 2019advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorchcited by 69
- 2020Curriculum Pre-training for End-to-End Speech Translationcited by 57
- 2020TetraTSDF: 3D human reconstruction from a single image with a tetrahedral outer shellcited by 18
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2 papers.