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This paper presents a different way to improve the resistance of digital watermarking. Using the well known Lattice QIM in the spatial domain, we analyze the interest of using a different kind of error correcting codes: rank metric codes. These codes are already used in communications for network coding but not used in the context of watermarking. In this article, we show how this metric permits to correct errors with a specific structure and is adapted to specific image attacks. We propose a first study to validate the concept of rank metric for watermarking process.


For about 10 years, detecting the presence of a secret message hidden
in an image was performed with an Ensemble Classifier trained
with Rich features. In recent years, studies such as Xu et al. have
indicated that well-designed Convolutional Neural Networks(CNN)
can achieve comparable performance to the two-step machine learning
In this paper we propose a CNN that outperforms the state-ofthe-
art in terms of error probability. The proposition is in the continuity
of what has been recently proposed and it is a clever fusion


The QR (Quick Response) code is a two-dimensional barcode, which was designed for storage information and high speed reading applications. Being cheap to produce and fast to read, it becomes actually a popular solution for product labeling.
Ones try to make QR code a solution against counterfeiting. We present a novel technique that permits to create a secure printed QR code which is robust


3D steganography is used in order to embed or hide information into 3D objects without causing visible or machine detectable modifications. In this paper we rethink about a high capacity 3D steganography based on the Hamiltonian path quantization, and increase its resistance to steganalysis. We analyze the parameters that may influence the distortion of a 3D shape as well as the resistance of the steganography to 3D steganalysis. According to the experimental results, the proposed high capacity 3D steganographic method has an increased resistance to steganalysis.


The terminology "multimedia security" gained a new popularity in the mid-90s with the rapid rise of digital watermarking in an attempt to combat piracy of copyrighted content. This milestone incarnates the mutation of content protection techniques from conventional cryptography to signal processing techniques. Today, multimedia security encompasses a much wider range of techniques such as multimedia encryption, content fingerprinting, anti-camcording, passive forensic analysis.


Forensic watermarking is a technology that deters piracy by providing means to copyright owners to trace back the identity of the original recipient of a video in case it appears on unauthorized sharing platforms. Historically, its use has been somewhat limited to professional environments e.g. for pre-release material movie distribution and in digital cinemas. MovieLabs recently released a specification that mandates the use of such forensic watermarks to deliver premium UHD content, thereby opening doors for mass market deployment of watermarking in CE environment.


Correlation-based watermark detection inherently assumes that a pseudo-random watermark pattern is present in the cover signal at a given location. However, disparity-coherent watermarks for stereo video content are broken into pieces which are offset one with respect to the others. Acoustic transmission yields echoes due to multi-path propagation and may thus introduce watermark replicates in the recorded audio track. In such cases, the watermark energy is not concentrated in a single element of the cross-correlation array but rather distributed over a number of them.