Object-Removal Forgery Detection Through Reflectance Analysis

International audienceWhile creating digital image forgeries, it is often necessary to hide an object from the image. For instance, to replace a person on a picture one would first remove the person on the image before inserting the new one. This process of suppression can be performed in many ways but almost always involve recreating some background textures. While recreating it, artists sometimes use common tools and apply smooth transitions to seamlessly blend the result. This operation can decrease the created texture sharpness. In this paper, we explore the possibility to reveal this effect to expose possible forgeries

Video-based assistive aid for blind people using object recognition in dissimilar frames

International audienc

Visual substitution system for room labels identification based on text detection and recognition

International audienc

Denoising of dynamic PET images using a multi-scale transform and non-local means filter

International audienc

An Image Forgery Detection Solution based on DCT Coefficient Analysis

International audienc

Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

International audienc

A Watermarking Technique to Secure Printed Matrix Barcode—Application for Anti-Counterfeit Packaging

International audienc

Face presentation attack detection based on a statistical model of image noise

International audienceThe vulnerability of most existing face recognition and authentication systems against face presentation attacks (a.k.a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced. The performance of the proposed approach was proven in the mentioned database. Experimental results show that, in a controlled situation, our solution performs better than the other approaches in the literature

Face spoofing attack detection based on the behavior of noises

International audienceThis paper aims to study the problem of spoofing attack detection for facial recognition systems. Real faces and falsified faces present in front of a security system (phone's camera in our case) have differences of micro-textures on their surface, which are exploited to discriminate face spoofing images. Our method exploits the statistic behavior of the distribution of noise's local variances, which performs differently between images of real faces and the fake ones. We test our method on two databases constructed in our laboratory. We used SVM for classification method. Experimental results show that the proposed method has an encouraging performance