Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact

• URL: http://arxiv.org/abs/2307.02055v1
• Date: Wed, 5 Jul 2023 06:40:08 GMT
• Title: Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact
• Authors: Jaydip Sen and Subhasis Dasgupta
• Abstract summary: This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN) CNNs are very popular deep-learning models which are used in image classification tasks. Two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task.

Related papers

• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Adversarial Attacks on Image Classification Models: Analysis and Defense [0.0]
  adversarial attacks on image classification models based on convolutional neural networks (CNN) Fast gradient sign method (FGSM) is explored and its adverse effects on the performances of image classification models are examined. mechanism is proposed to defend against the FGSM attack based on a modified defensive distillation-based approach.
  arXiv  Detail & Related papers  (2023-12-28T08:08:23Z)
• Counterfactual Image Generation for adversarially robust and interpretable Classifiers [1.3859669037499769]
  We propose a unified framework leveraging image-to-image translation Generative Adrial Networks (GANs) to produce counterfactual samples. This is achieved by combining the classifier and discriminator into a single model that attributes real images to their respective classes and flags generated images as "fake" We show how the model exhibits improved robustness to adversarial attacks, and we show how the discriminator's "fakeness" value serves as an uncertainty measure of the predictions.
  arXiv  Detail & Related papers  (2023-10-01T18:50:29Z)
• Semantic Image Attack for Visual Model Diagnosis [80.36063332820568]
  In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images.
  arXiv  Detail & Related papers  (2023-03-23T03:13:04Z)
• Multiclass ASMA vs Targeted PGD Attack in Image Segmentation [0.0]
  This paper explores the projected gradient descent (PGD) attack and the Adaptive Mask Attack (ASMA) on the image segmentation DeepLabV3 model. The existence of such attack however puts all of image classification deep learning networks in danger of exploitation.
  arXiv  Detail & Related papers  (2022-08-03T05:05:30Z)
• Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification [17.881134865491063]
  Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs) This work analyzes adversarial attacks from a different perspective. Namely, adversarial examples contain implicit information that is useful to the predictions. We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions.
  arXiv  Detail & Related papers  (2022-03-11T08:48:26Z)
• Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation [123.33816363589506]
  We show the existence of a textbftraining-free adversarial perturbation under the no-box threat model. Motivated by our observation that high-frequency component (HFC) domains in low-level features, we attack an image mainly by manipulating its frequency components. Our method is even competitive to mainstream transfer-based black-box attacks.
  arXiv  Detail & Related papers  (2022-03-09T09:51:00Z)
• Deep Image Destruction: A Comprehensive Study on Vulnerability of Deep Image-to-Image Models against Adversarial Attacks [104.8737334237993]
  We present comprehensive investigations into the vulnerability of deep image-to-image models to adversarial attacks. For five popular image-to-image tasks, 16 deep models are analyzed from various standpoints. We show that unlike in image classification tasks, the performance degradation on image-to-image tasks can largely differ depending on various factors.
  arXiv  Detail & Related papers  (2021-04-30T14:20:33Z)
• 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)
• Towards Class-Oriented Poisoning Attacks Against Neural Networks [1.14219428942199]
  Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset. We propose a class-oriented poisoning attack that is capable of forcing the corrupted model to predict in two specific ways. To maximize the adversarial effect as well as reduce the computational complexity of poisoned data generation, we propose a gradient-based framework.
  arXiv  Detail & Related papers  (2020-07-31T19:27:37Z)
• Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes [51.31334977346847]
  We train networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction. We demonstrate that, by imposing consistency on the representations learned across differently quantized images, the adversarial robustness of networks improves significantly.
  arXiv  Detail & Related papers  (2020-04-01T09:31:10Z)

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.