text: Deep neural networks (DNNs) have shown phenomenal success in a wide range of applications. However, recent studies have discovered that they are vulnerable to Adversarial Examples, i.e., original samples with added subtle perturbations. Such perturbations are often too small and imperceptible to humans, yet they can easily fool the neural networks. Few defense techniques against adversarial examples have been proposed, but they require modifying the target model or prior knowledge of adversarial examples generation methods. Likewise, their performance remarkably drops upon encountering adversarial example types not used during the training stage. In this paper, we propose a new framework that can be used to enhance DNNs' robustness by detecting adversarial examples. In particular, we employ the decision layer of independently trained models as features for posterior detection. The proposed framework doesn't require any prior knowledge of adversarial examples generation techniques, and can be directly augmented with unmodified off-the-shelf models. Experiments on the standard MNIST and CIFAR10 datasets show that it generalizes well across not only different adversarial examples generation methods but also various additive perturbations. Specifically, distinct binary classifiers trained on top of our proposed features can achieve a high detection rate (>90 ) in a set of white-box attacks and maintain this performance when tested against unseen attacks.
  @cite_26: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.
  @cite_33: 
  @cite_24: Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input @math and any target classification @math , it is possible to find a new input @math that is similar to @math but classified as @math . This makes it difficult to apply neural networks in security-critical areas. Defensive distillation is a recently proposed approach that can take an arbitrary neural network, and increase its robustness, reducing the success rate of current attacks' ability to find adversarial examples from @math to @math . In this paper, we demonstrate that defensive distillation does not significantly increase the robustness of neural networks by introducing three new attack algorithms that are successful on both distilled and undistilled neural networks with @math probability. Our attacks are tailored to three distance metrics used previously in the literature, and when compared to previous adversarial example generation algorithms, our attacks are often much more effective (and never worse). Furthermore, we propose using high-confidence adversarial examples in a simple transferability test we show can also be used to break defensive distillation. We hope our attacks will be used as a benchmark in future defense attempts to create neural networks that resist adversarial examples.
  @cite_25: In recent years, the widespread use of deep neural networks (DNNs) has facilitated great improvements in performance for computer vision tasks like image classification and object recognition. In most realistic computer vision applications, an input image undergoes some form of image distortion such as blur and additive noise during image acquisition or transmission. Deep networks trained on pristine images perform poorly when tested on such distortions. In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters. We propose a metric to identify the most noise susceptible convolutional filters and rank them in order of the highest gain in classification accuracy upon correction. In our proposed approach called DeepCorrect , we apply small stacks of convolutional layers with residual connections at the output of these ranked filters and train them to correct the worst distortion affected filter activations, while leaving the rest of the pre-trained filter outputs in the network unchanged. Performance results show that applying DeepCorrect models for common vision tasks like image classification (ImageNet), object recognition (Caltech-101, Caltech-256), and scene classification (SUN-397), significantly improves the robustness of DNNs against distorted images and outperforms other alternative approaches.
  @cite_17: This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Specifically, we study applying image transformations such as bit-depth reduction, JPEG compression, total variance minimization, and image quilting before feeding the image to a convolutional network classifier. Our experiments on ImageNet show that total variance minimization and image quilting are very effective defenses in practice, in particular, when the network is trained on transformed images. The strength of those defenses lies in their non-differentiable nature and their inherent randomness, which makes it difficult for an adversary to circumvent the defenses. Our best defense eliminates 60 of strong white-box and 90 of strong black-box attacks by a variety of major attack methods
summary: The proposed defenses for mitigating AEs can be grouped into two categories. The first category techniques try either improving the robustness of DNNs or suppressing the success rates of attacks. For instance, @cite_26 , which is training the system with AEs to augment the regularization and loss functions and making the system more resilient. The other technique is @cite_33 in which additional DNNs with softmax are trained to obstruct the deep learning system from fitting too tightly to the data. However, it has been demonstrated that defensive distillation method can be easily circumvented with a minimal modified attack @cite_24 . Other approaches are or @cite_17 , i.e., removing the adversarial noise from the input samples before feeding them to neural networks, and @cite_25 , i.e., modifying the traditional neural network architectures, e.g., adding extra specific robust layers and functions.