Residual Generative Network Research Articles

Removing Snow From a Single Image Using a Residual Frequency Module and Perceptual RaLSGAN

Removing snow particles from an image is a complicated task due to the particles’ shape, size, and color. The latest snow removal methods remove snow from a single image but retain some snow and salt-and-pepper particles. Some approaches, while trying to remove snow from a single image, produce blurry artifacts. In this paper, we solve these problems by designing a network model that consists of a residual generative network, a snow-free image generative network, and a perceptual relativistic discriminative network. In both generative networks, we assign the residual frequency network (ReFNet) as our bottleneck module. Our network model learns to map two relationships. First, the input snowy image is trained to map the snow mask image in the dataset. Then, a retained image resulting from subtraction between an input image and the estimated residual image is concatenated with the input snowy image and mapped to the desired snow-free ground truth. Moreover, we use a perceptual identical-paired adversarial network based on a relativistic discriminative network to make our training results more robust. Our results achieve greater performance than state-of-the-art methods on both synthetic and real-world snowy images.

Complete Defense Framework to Protect Deep Neural Networks against Adversarial Examples

Although Deep Neural Networks (DNNs) have achieved great success on various applications, investigations have increasingly shown DNNs to be highly vulnerable when adversarial examples are used as input. Here, we present a comprehensive defense framework to protect DNNs against adversarial examples. First, we present statistical and minor alteration detectors to filter out adversarial examples contaminated by noticeable and unnoticeable perturbations, respectively. Then, we ensemble the detectors, a deep Residual Generative Network (ResGN), and an adversarially trained targeted network, to construct a complete defense framework. In this framework, the ResGN is our previously proposed network which is used to remove adversarial perturbations, and the adversarially trained targeted network is a network that is learned through adversarial training. Specifically, once the detectors determine an input example to be adversarial, it is cleaned by ResGN and then classified by the adversarially trained targeted network; otherwise, it is directly classified by this network. We empirically evaluate the proposed complete defense on ImageNet dataset. The results confirm the robustness against current representative attacking methods including fast gradient sign method, randomized fast gradient sign method, basic iterative method, universal adversarial perturbations, DeepFool method, and Carlini & Wagner method.

RGN-Defense: erasing adversarial perturbations using deep residual generative network

In recent years, deep neural networks have achieved great success in various fields, especially in computer vision. However, recent investigations have shown that current state-of-the-art classification models are highly vulnerable to adversarial perturbations contained in the input examples. Therefore, we propose a defense methodology against the adversarial perturbations. Prior to a targeted network, adversarial perturbations are erased or mitigated via a deep residual generative network (RGN). Through adopting an auxiliary network VGG-19, the RGN is trained toward optimization of a joint loss, including low-level pixel loss, middle-level texture loss, and high-level task loss, thereby the restored examples are highly consistent with the original legitimate examples. We call our proposed defense based on RGN as RGN-Defense. It is an independent defense module that can be flexibly integrated with other defense strategy, for example, adversarial training, to construct a more powerful defense system. In the experiment, we evaluate our approach on ImageNet, and the comprehensive results have demonstrated robustness of RGN-Defense against current representative attacks.