An adaptive perception-based image preprocessing method

The aim of this paper is to introduce an adaptive preprocessing procedure based on human perception in order to increase the performance of some standard image processing techniques. Specifically, image frequency content has been weighted by the corresponding value of the contrast sensitivity function, in agreement with the sensitiveness of human eye to the different image frequencies and contrasts. The 2D Rational dilation wavelet transform has been employed for representing image frequencies. In fact, it provides an adaptive and flexible multiresolution framework, enabling an easy and straightforward adaptation to the image frequency content. Preliminary experimental results show that the proposed preprocessing allows us to increase the performance of some standard image enhancement algorithms in terms of visual quality and often also in terms of PSNR

A Broad Spectrum Defense Against Adversarial Examples

Machine learning models are increasingly employed in making critical decisions across a wide array of applications. As our dependence on these models increases, it is vital to recognize their vulnerability to malicious attacks from determined adversaries. In response to these adversarial attacks, new defensive mechanisms have been developed to ensure the security of machine learning models and the accuracy of the decisions they make. However, many of these mechanisms are reactionary, designed to defend specific models against a known specific attack or family of attacks. This reactionary approach does not generalize to future yet to be developed attacks. In this work, we developed Broad Spectrum Defense (BSD) as a defensive mechanism to secure any model against a wide range of attacks. BSD is not reactionary, and unlike most other approaches, it does not train its detectors using adversarial data, hence removing an inherent bias present in other defenses that rely on having access to adversarial data. An extensive set of experiments showed that BSD outperforms existing detector-based methods such as MagNet and Feature Squeezing. We believe BSD will inspire a new direction in adversarial machine learning to create a robust defense capable of generalizing to existing and future attacks

Estimating Watermarking Capacity in Gray Scale Images Based on Image Complexity

Capacity is one of the most important parameters in image watermarking. Different works have been done on this subject with different assumptions on image and communication channel. However, there is not a global agreement to estimate watermarking capacity. In this paper, we suggest a method to find the capacity of images based on their complexities. We propose a new method to estimate image complexity based on the concept of Region Of Interest (ROI). Our experiments on 2000 images showed that the proposed measure has the best adoption with watermarking capacity in comparison with other complexity measures. In addition, we propose a new method to calculate capacity using proposed image complexity measure. Our proposed capacity estimation method shows better robustness and image quality in comparison with recent works in this field

The Perception-Distortion Tradeoff

Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at: https://youtu.be/_aXbGqdEkjk?t=39m43

Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

BayBFed: Bayesian Backdoor Defense for Federated Learning

Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks such as backdoor attacks. Consequently, a variety of defenses have recently been proposed, which have primarily utilized intermediary states of the global model (i.e., logits) or distance of the local models (i.e., L2-norm) from the global model to detect malicious backdoors. However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies. In this paper, we introduce a novel and more generic backdoor defense framework, called BayBFed, which proposes to utilize probability distributions over client updates to detect malicious updates in FL: it computes a probabilistic measure over the clients' updates to keep track of any adjustments made in the updates, and uses a novel detection algorithm that can leverage this probabilistic measure to efficiently detect and filter out malicious updates. Thus, it overcomes the shortcomings of previous approaches that arise due to the direct usage of client updates; as our probabilistic measure will include all aspects of the local client training strategies. BayBFed utilizes two Bayesian Non-Parametric extensions: (i) a Hierarchical Beta-Bernoulli process to draw a probabilistic measure given the clients' updates, and (ii) an adaptation of the Chinese Restaurant Process (CRP), referred by us as CRP-Jensen, which leverages this probabilistic measure to detect and filter out malicious updates. We extensively evaluate our defense approach on five benchmark datasets: CIFAR10, Reddit, IoT intrusion detection, MNIST, and FMNIST, and show that it can effectively detect and eliminate malicious updates in FL without deteriorating the benign performance of the global model

Deep Semantic Statistics Matching (D2SM) Denoising Network

The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learning like pixel-wise losses is performed in a sample-to-sample manner, which ignores the intrinsic correlation of images, especially semantics. In this paper, we introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks, and the denoised results can be better understood by high-level vision tasks. Comprehensive experiments conducted on the noisy Cityscapes dataset demonstrate the superiority of our method on both the denoising performance and semantic segmentation accuracy. Moreover, the performance improvement observed on our extended tasks including super-resolution and dehazing experiments shows its potentiality as a new general plug-and-play component.Comment: ECCV2022, for Project Page, see https://kfmei.page/d2sm