Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Chen, Tianlong; Liu, Sijia; Chang, Shiyu; Cheng, Yu; Amini, Lisa; Wang, Zhangyang

doi:10.1109/cvpr42600.2020.00078

Cited by 160 publications

(137 citation statements)

References 10 publications

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“…Naseer et al [342] proposed self-supervised adversarial training, whereas adversarial training is independently analyzed for self-supervision by incorporating it in pretraining in [343]. Similarly, [344] [345], which uses perturbations in the image space as well as latent space of StyleGAN to make training more effective.…”

Section: A Model Alteration For Defensementioning

confidence: 99%

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2018

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Deep learning is at the heart of the current rise of artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has recently lead to a large influx of contributions in this direction. This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

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Section: A Model Alteration For Defensementioning

confidence: 99%

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2018

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“…Semi-and weak supervision could also be introduced into RGB-D salient object detection, by leveraging image-level tags [185] and pseudo pixel-wise annotations [188,190], to improve detection performance. Furthermore, several studies [191,192] have suggested that models pretrained using self-supervision can effectively be used to achieve better performance. Therefore, we could train saliency prediction models on large amounts of annotated RGB images in a self-supervised manner and then transfer the pre-trained models to the RGB-D salient object detection task.…”

Section: Different Supervision Strategiesmentioning

confidence: 99%

RGB-D salient object detection: A survey

et al. 2021

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Salient object detection, which simulates human visual perception in locating the most significant object(s) in a scene, has been widely applied to various computer vision tasks. Now, the advent of depth sensors means that depth maps can easily be captured; this additional spatial information can boost the performance of salient object detection. Although various RGB-D based salient object detection models with promising performance have been proposed over the past several years, an in-depth understanding of these models and the challenges in this field remains lacking. In this paper, we provide a comprehensive survey of RGB-D based salient object detection models from various perspectives, and review related benchmark datasets in detail. Further, as light fields can also provide depth maps, we review salient object detection models and popular benchmark datasets from this domain too. Moreover, to investigate the ability of existing models to detect salient objects, we have carried out a comprehensive attribute-based evaluation of several representative RGB-D based salient object detection models. Finally, we discuss several challenges and open directions of RGB-D based salient object detection for future research. All collected models, benchmark datasets, datasets constructed for attribute-based evaluation, and related code are publicly available at https://github.com/taozh2017/RGBD-SODsurvey.

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“…The labeling and sample efficiency challenges of deep learning, in fact, are further exacerbated by its vulnerability to adversarial attacks. The sample complexity of learning an adversarially robust model with current methods is significantly higher than that of standard learning [16]. Additionally, AT-based techniques have been observed to cause an undesirable decline in standard accuracy (the classification accuracy on unperturbed inputs) while increasing robust accuracy (the classification accuracy on worst-case perturbed inputs) [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al [18] improved robustness by learning representations that were consistent under both augmented data and adversarial examples. Chen et al [16] generalized adversarial training to different self-supervised pretraining and fine-tuning schemes.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Self-Supervised Learning for Robust SAR Target Recognition

Sun

Chen

et al. 2021

Remote Sensing

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Synthetic aperture radar (SAR) can perform observations at all times and has been widely used in the military field. Deep neural network (DNN)-based SAR target recognition models have achieved great success in recent years. Yet, the adversarial robustness of these models has received far less academic attention in the remote sensing community. In this article, we first present a comprehensive adversarial robustness evaluation framework for DNN-based SAR target recognition. Both data-oriented metrics and model-oriented metrics have been used to fully assess the recognition performance under adversarial scenarios. Adversarial training is currently one of the most successful methods to improve the adversarial robustness of DNN models. However, it requires class labels to generate adversarial attacks and suffers significant accuracy dropping on testing data. To address these problems, we introduced adversarial self-supervised learning into SAR target recognition for the first time and proposed a novel unsupervised adversarial contrastive learning-based defense method. Specifically, we utilize a contrastive learning framework to train a robust DNN with unlabeled data, which aims to maximize the similarity of representations between a random augmentation of a SAR image and its unsupervised adversarial example. Extensive experiments on two SAR image datasets demonstrate that defenses based on adversarial self-supervised learning can obtain comparable robust accuracy over state-of-the-art supervised adversarial learning methods.

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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

Cited by 160 publications

References 10 publications

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

RGB-D salient object detection: A survey

Adversarial Self-Supervised Learning for Robust SAR Target Recognition

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