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The 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.

Counterfeiting of consumer goods is a critical problem which causes various negative impacts for consumers, enterprises and the whole economic ecosystem. Anti-counterfeit labels and packaging is one of the main solutions to help enterprises protect their brand against this concern. In this paper, we introduced a novel watermarking technique, which embeds a particular random micro-texture into a matrix barcode and transforms this one into a security layer for making packaging anti-counterfeited. The secured matrix barcode is hard to be reproduced by counterfeiters. In fact, any degradations caused by the counterfeiting process will change the statistical behaviors of the embedded micro-texture. Statistical detectors based on the hypothesis testing framework are also introduced to classify authentic and counterfeited printed barcodes. Experimental results confirm the usability and the effectiveness of the proposed anti-counterfeiting solution.INDEX TERMS Anti-counterfeiting, printed watermarking, matrix code, statistical hypothesis testing. III. DESCRIPTION OF W-QR CODEA. CONCEPT

Copy-Move forgery has been widely studied as it is a really common forgery. Furthermore, it is the easiest forgery to create with serious security-related threats in particular for distant remote id onboarding where company ask their customer to send a photo of their ID document. It is then easy for a counterfeit to alter the information on the document by copying and pasting letters within the photo. On the other hand, copy-move detection algorithms are known to perform worse in presence of similar but genuine objects preventing us from using them in practical situations like remote ID onboarding. In this article we propose a novel copy-move public dataset containing forged ID documents and study current state-of-the-art performances on this dataset to evaluate their potential use in practical situations.

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