In this paper, we propose a new comprehensive Video Surveillance Quality Assessment Dataset (VSQuAD) dedicated to Video Surveillance (VS) systems. In contrast to other public datasets, this one contains many more videos with distortions and diversified content from common video surveillance scenarios. These videos have been artificially degraded with various types of distortions (single distortion or multiple distortions simultaneously) at different severity levels. In order to improve the efficiency of the surveillance systems and the versatility of the video quality assessment dataset, night vision CCTV videos are also included. Furthermore, a comprehensive analysis of the content in terms of diversity and challenging problems is also presented in this study. The interest of such database is twofold. First, it will serve for benchmarking different video distortion detection and classification algorithms. Second, it will be useful for the design of learning models for various challenging VS problems such as identification and removal of the most common distortions. The complete dataset is made publicly available as part of a challenge session in this conference through the following link: https://www.l2ti.univ-paris13.fr/VSQuad/.

An effective contrast enhancement method should not only improve the perceptual quality of an image but should also avoid adding any artifacts or affecting naturalness of images. This makes Contrast Enhancement Evaluation (CEE) a challenging task in the sense that both the improvement in image quality and unwanted side-effects need to be checked for. Currently, there is no single CEE metric that works well for all kinds of enhancement criteria. In this paper, we propose a new Multi-Criteria CEE (MCCEE) measure which combines different metrics effectively to give a single quality score. In order to fully exploit the potential of these metrics, we have further proposed to apply them on the decomposed image using wavelet transform. This new metric has been tested on two natural image contrast enhancement databases as well as on medical Computed Tomography (CT) images. The results show a substantial improvement as compared to the existing evaluation metrics. The code for the metric is available at: https://github.com/zakopz/MCCEE-Contrast-Enhancement-Metric.

A plethora of Contrast Enhancement (CE) methods has been proposed in the literature. Each of these has its own strengths and limitations. Further, the quality of the resulting enhanced images depends upon the original image and its content. Hence, a given CE method can provide good quality for a certain image but a poorer quality for another. In this paper, we propose a novel workflow to provide an automatic ranking of enhanced images which may have been obtained using different techniques. The proposed technique is based on a Convolutional Neural Network (CNN) using saliency information. The idea is to start by comparing two enhanced versions of a given image in order to select the best one automatically based on perceived quality. Here, a saliency map is used to select relevant patches which are highly correlated with the human visual system sensitivity. The well-known Structural Similarity Image Metric (SSIM) map is also employed to compare the similarity between both enhanced images. Using such information, a CNN model is trained to predict the rank in terms of the image quality as perceived by humans. The algorithm is tested over three CE benchmarking databases with the experimental results validating the superiority of the proposed system as compared to state-of-the-art CE evaluation techniques.

With the massive increase of online content, widespread of social media, the popularity of smartphones, and rise of security breaches, image forensics has attracted a lot of attention in the past two decades alongside the advancements in digital imaging and processing software. The goal is to be able to verify authenticity, ownership, and copyright of an image and detect changes to the original image. However, more sophisticated image manipulation software tools can use subtle anti‐forensics techniques (AFTs) to complicate and hinder detection. This leads security professionals and digital investigators to develop more robust forensics tools and counter solutions to defeat adversarial anti‐forensics and win the race. This survey study presents a comprehensive systematic overview of various anti‐forensics and anti‐AFTs that are proposed in the literature for digital image forensics. These techniques are thoroughly analysed based on various important characteristics and grouped into broad categories. This study also presents a bibliographic analysis of the‐state‐of‐the‐art publications in various venues. It assists junior researchers in multimedia security and related fields to understand the significance of existing techniques, research trends, and future directions.