Maher El'arbi

Video Watermarking Based on Neural Networks

In this paper, we propose a novel digital video watermarking scheme based on multi resolution motion estimation and artificial neural network. A multi resolution motion estimation algorithm is adopted to preferentially allocate the watermark to coefficients containing motion. In addition, embedding and extraction of the watermark are based on the relationship between a wavelet coefficient and its neighbors. A neural network is given to memorize the relationships between coefficients in a 3times3 block of the image. Experimental results show that embedding watermark where picture content is moving is less perceptible. Further, it shows that the proposed scheme is robust against common video processing attacks

A dynamic video watermarking algorithm in fast motion areas in the wavelet domain

In this paper, we propose a video watermarking algorithm which embeds different parts of a single watermark into different shots of a video under the wavelet domain. Based on a Motion Activity Analysis, different regions of the original video are separated into perceptually distinct categories according to motion information and region complexity. Thus, the localizations of the watermark are adjusted adaptively in accordance with the human visual system and signal characteristics, which makes them perceptually invisible and less vulnerable to automated removal. In addition, contrary to traditional methods where the watermark remains at a fixed position on the screen, the watermark moves along with moving objects and thus motion artefacts can be avoid. The multi-frame based extraction strategy ensures that the watermark can be correctly recovered from a very short segment of video. Individual frames extracted from the video also contain watermark information. Experimental results show that the inserted watermark is not only less perceptible but also robust against common video processing attacks.

Collusion, MPEG4 compression and frame dropping resistant video watermarking

We present in this article a new video watermarking which resists collusion, MPEG4 compression and frame dropping attacks. This scheme is based on video mosaicing. For that, we are going to start by describing the mosaicing technique in order to illustrate the contribution of this technique in video watermarking. In fact, mosaicing allows to select an interesting area where the mark should be embedded. The idea is to insert the same mark into the same pixels which represent the same physical point. This is exactly the information which can be provided by a mosaic image at least for the located points in the scene background. Next, we present extensive experimental simulations which prove the watermark imperceptibility and robustness against several video attacks.

A new DCT audio watermarking scheme based on preliminary MP3 study

In this paper, a new audio watermarking scheme operating in the frequency domain and based on neural network architecture is described. The watermark is hidden into the middle frequency band after performing a Discrete Cosine transform (DCT). Embedding and extraction of the watermark are based on the use of a back-propagation neural network (BPNN) architecture. In addition, the selection of frequencies and the block hiding the watermark are based on a preliminary study of the effect of MP3 compression at several rates on the signal. Experimental results show that the proposed technique presents good robustness and perceptual quality results. We also investigate the application of the proposed technique in video watermarking. Traditional techniques have used audio channel as supplementary embedding space and adopt state-of-the art techniques that resist to MP3 compression attack. In these techniques, the MPEG compression attack is only evaluated on the video part and the audio part is kept unaffected. In this paper, we adapt the preliminary MP3 study to video watermarking technique but with a preliminary study of the MPEG compression applied to the audio channel. Here again, we notice that the application of the preliminary MPEG study to the audio channel improves the robustness of the video watermarking scheme though keeping high-quality watermarked video sequences.

Regularized Shearlet Network for face recognition using single sample per person

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach includes a module based on regularization theory (RSN) to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-arts method using several facial databases, such as AR, FERET, FRGC, FEI, CK. Our tests show that the RSN approach is very competitive and outperforms several standard face recognition methods.

A Video Watermarking Scheme Resistant to Geometric Transformations

This paper describes a blind video watermarking system invariant to geometrical attacks. Our scheme embeds different parts of a single watermark into different shots of a video under the wavelet domain. A multi resolution motion estimation algorithm (MRME) is adopted to preferentially allocate the watermark to coefficients containing motion. In addition, embedding and extraction of the watermark are based on the relationship between a coefficient and its neighbors. Experimental results show that inserting watermark where picture content is moving is less perceptible. Further, it shows that the proposed scheme is robust against common video processing attacks.

Multiresolution motion estimation and compensation for video coding

Recently, the quantity of data has known a big evolution especially with the emergence of many video applications over networks such as the videophone and the videoconferencing, and multimedia devices such as the high-definition TV and the personal digital assistants. So, it was crucial to reduce the quantity of data stored or transmitted by compressing it spatially and temporally. Hence, motion estimation and compensation are employed in video coding systems to remove temporal redundancy while keeping a high visual quality. They are the most important parts of the video coding process since they require the most computational power and the biggest consumption in resources and bandwidth. Therefore, many techniques have been developed to estimate motion between successive frames. In this paper, we will present our motion estimation and compensation method applied on the discrete wavelet transform coefficients and based on the block matching algorithm which is the simplest, the most efficient and the most popular technique. Additional techniques are introduced to accelerate the estimation process and improve the prediction quality.

Image authentication algorithm with recovery capabilities based on neural networks in the DCT domain

In this study, the authors propose an image authentication algorithm in the DCT domain based on neural networks. The watermark is constructed from the image to be watermarked. It consists of the average value of each 8 × 8 block of the image. Each average value of a block is inserted in another supporting block sufficiently distant from the protected block to prevent simultaneous deterioration of the image and the recovery data during local image tampering. Embedding is performed in the middle frequency coefficients of the DCT transform. In addition, a neural network is trained and used later to recover tampered regions of the image. Experimental results shows that the proposed method is robust to JPEG compression and can also not only localise alterations but also recover them.

A Dynamic Video Watermarking Scheme in the DWT Domain

In this paper, a novel dynamic video watermarking is proposed. It takes full advantage of motion information of video content to guarantee the perceptual invisibility and robustness of the watermark. A major advantage of this technique is that the watermark is no longer at a fixed position on the screen, but moves along with moving coefficients. Embedding and extraction of the watermark are based on the wavelet network paradigm. Experimental results show that the watermarked video appears visually indistinguishable from the original video, and the proposed watermarking technique is robust enough to common video attacks.

Regularized directional feature learning for face recognition

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach is augmented by regularization theory (RSN) in order to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-art methods.