scholarly journals Unsupervised Adversarial Defense through Tandem Deep Image Priors

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1957
Author(s):  
Yu Shi ◽  
Cien Fan ◽  
Lian Zou ◽  
Caixia Sun ◽  
Yifeng Liu
Keyword(s):  
Image Restoration ◽  
Deep Neural Networks ◽  
Defense Mechanism ◽  
Original Image ◽  
Image Prior ◽  
Wrong Prediction ◽  
Previous State ◽  
Deep Image ◽  
Image Priors ◽  
Adversarial Example

Deep neural networks are vulnerable to the adversarial example synthesized by adding imperceptible perturbations to the original image but can fool the classifier to provide wrong prediction outputs. This paper proposes an image restoration approach which provides a strong defense mechanism to provide robustness against adversarial attacks. We show that the unsupervised image restoration framework, deep image prior, can effectively eliminate the influence of adversarial perturbations. The proposed method uses multiple deep image prior networks called tandem deep image priors to recover the original image from adversarial example. Tandem deep image priors contain two deep image prior networks. The first network captures the main information of images and the second network recovers original image based on the prior information provided by the first network. The proposed method reduces the number of iterations originally required by deep image prior network and does not require adjusting the classifier or pre-training. It can be combined with other defensive methods. Our experiments show that the proposed method surprisingly achieves higher classification accuracy on ImageNet against a wide variety of adversarial attacks than previous state-of-the-art defense methods.

scholarly journals DIP-VBTV: A Color Image Restoration Model Combining a Deep Image Prior and a Vector Bundle Total Variation

2021 ◽  
Vol 14 (4) ◽  
pp. 1816-1847
Author(s):  
Thomas Batard ◽  
Gloria Haro ◽  
Coloma Ballester
Keyword(s):  
Vector Bundle ◽  
Image Restoration ◽  
Total Variation ◽  
Color Image ◽  
Model Combining ◽  
Image Prior ◽  
Deep Image ◽  
Restoration Model

scholarly journals Image Denoising Using Nonlocal Regularized Deep Image Prior

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2114
Author(s):  
Zhonghua Xie ◽  
Lingjun Liu ◽  
Zhongliang Luo ◽  
Jianfeng Huang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Image Denoising ◽  
Deep Neural Networks ◽  
Optimization Problems ◽  
Training Data ◽  
Superior Performance ◽  
Convolutional Network ◽  
Image Prior ◽  
Deep Image

Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an unsupervised deep-learning-based method. Specifically, we propose a deep-learning-based method based on the deep image prior (DIP) method, which only requires a noisy image as training data, without any clean data. It infers the natural images with random inputs and the corrupted observation with the help of performing correction via a convolutional network. We improve the original DIP method as follows: Firstly, the original optimization objective function is modified by adding nonlocal regularizers, consisting of a spatial filter and a frequency domain filter, to promote the gradient sparsity of the solution. Secondly, we solve the optimization problem with the alternating direction method of multipliers (ADMM) framework, resulting in two separate optimization problems, including a symmetric U-Net training step and a plug-and-play proximal denoising step. As such, the proposed method exploits the powerful denoising ability of both deep neural networks and nonlocal regularizations. Experiments validate the effectiveness of leveraging a combination of DIP and nonlocal regularizers, and demonstrate the superior performance of the proposed method both quantitatively and visually compared with the original DIP method.

scholarly journals OCT Image Restoration Using Non-Local Deep Image Prior

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 784
Author(s):  
Wenshi Fan ◽  
Hancheng Yu ◽  
Tianming Chen ◽  
Sheng Ji
Keyword(s):  
Image Restoration ◽  
Image Denoising ◽  
Superior Performance ◽  
Quality Data ◽  
Speckle Reduction ◽  
Low Level ◽  
Image Prior ◽  
Non Local ◽  
Deep Image ◽  
Oct Image

In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However, the DIP has difficulty finding a good balance between maintaining details and suppressing speckle noise. Inspired by DIP, in this paper, a sorted non-local statics which measures the signal autocorrelation in the differences between the constructed image and the input image is proposed for OCT image restoration. By adding the sorted non-local statics as a regularization loss in the DIP learning, more low-level image statistics are captured by CNN networks in the process of OCT image restoration. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual quality.

Image Restoration with Structured Deep Image Prior

2021 ◽  
Author(s):  
Jikai Li ◽  
Ruiki Kobayashi ◽  
Shogo Muramatsu ◽  
Gwanggil Jeon
Keyword(s):  
Image Restoration ◽  
Image Prior ◽  
Deep Image

scholarly journals Using Deep Image Priors to Generate Counterfactual Explanations

2021 ◽  
Author(s):  
Vivek Narayanaswamy ◽  
Jayaraman J. Thiagarajan ◽  
Andreas Spanias
Keyword(s):  
Deep Image ◽  
Image Priors

scholarly journals Real-Time Adversarial Attack Detection with Deep Image Prior Initialized as a High-Level Representation Based Blurring Network

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 52
Author(s):  
Richard Evan Sutanto ◽  
Sukho Lee
Keyword(s):  
Neural Network ◽  
Attack Detection ◽  
Detection Methods ◽  
Defense System ◽  
Image Prior ◽  
The Neural Network ◽  
Adversarial Examples ◽  
Deep Image ◽  
Adversarial Attack ◽  
High Level

Several recent studies have shown that artificial intelligence (AI) systems can malfunction due to intentionally manipulated data coming through normal channels. Such kinds of manipulated data are called adversarial examples. Adversarial examples can pose a major threat to an AI-led society when an attacker uses them as means to attack an AI system, which is called an adversarial attack. Therefore, major IT companies such as Google are now studying ways to build AI systems which are robust against adversarial attacks by developing effective defense methods. However, one of the reasons why it is difficult to establish an effective defense system is due to the fact that it is difficult to know in advance what kind of adversarial attack method the opponent is using. Therefore, in this paper, we propose a method to detect the adversarial noise without knowledge of the kind of adversarial noise used by the attacker. For this end, we propose a blurring network that is trained only with normal images and also use it as an initial condition of the Deep Image Prior (DIP) network. This is in contrast to other neural network based detection methods, which require the use of many adversarial noisy images for the training of the neural network. Experimental results indicate the validity of the proposed method.

Delving into Deep Image Prior for Adversarial Defense

2021 ◽  
Author(s):  
Li Ding ◽  
Yongwei Wang ◽  
Xin Ding ◽  
Kaiwen Yuan ◽  
Ping Wang ◽  
...  
Keyword(s):  
Image Prior ◽  
Deep Image

scholarly journals Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

2021 ◽  
pp. 1-1
Author(s):  
Fangshu Yang ◽  
Thanh-an Pham ◽  
Nathalie Brandenberg ◽  
Matthias P. Lutolf ◽  
Jianwei Ma ◽  
...  
Keyword(s):  
Phase Unwrapping ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Image Prior ◽  
Deep Image

Interpretable Deep Image Prior Method Inspired In Linear Mixture Model For Compressed Spectral Image Recovery

2021 ◽  
Author(s):  
Tatiana Gelvez ◽  
Jorge Bacca ◽  
Henry Arguello
Keyword(s):  
Mixture Model ◽  
Image Recovery ◽  
Spectral Image ◽  
Image Prior ◽  
Linear Mixture Model ◽  
Deep Image
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