Implementation of Adaptive Unsharp Masking as a pre-filtering method for watermark detection and extraction

Digital watermarking has been one of the focal points of research interests in order to provide multimedia security in the last decade. Watermark data, belonging to the user, are embedded on an original work such as text, audio, image, and video and thus, product ownership can be proved. Various robust watermarking algorithms have been developed in order to extract/detect the watermark against such attacks. Although watermarking algorithms in the transform domain differ from others by different combinations of transform techniques, it is difficult to decide on an algorithm for a specific application. Therefore, instead of developing a new watermarking algorithm with different combinations of transform techniques, we propose a novel and effective watermark extraction and detection method by pre-filtering, namely Adaptive Unsharp Masking (AUM). In spite of the fact that Unsharp Masking (UM) based pre-filtering is used for watermark extraction/detection in the literature by causing the details of the watermarked image become more manifest, effectiveness of UM may decrease in some cases of attacks. In this study, AUM has been proposed for pre-filtering as a solution to the disadvantages of UM. Experimental results show that AUM performs better up to 11\% in objective quality metrics than that of the results when pre-filtering is not used. Moreover; AUM proposed for pre-filtering in the transform domain image watermarking is as effective as that of used in image enhancement and can be applied in an algorithm-independent way for pre-filtering in transform domain image watermarking

Data Hiding of Motion Information in Chroma and Luma Samples for Video Compression

International audience2010 appears to be the launching date for new compression activities intended to challenge the current video compression standard H.264/AVC. Several improvements of this standard are already known like competition-based motion vector prediction. However the targeted 50% bitrate saving for equivalent quality is not yet achieved. In this context, this paper proposes to reduce the signaling information resulting from this vector competition, by using data hiding techniques. As data hiding and video compression traditionally have contradictory goals, a study of data hiding is first performed. Then, an efficient way of using data hiding for video compression is proposed. The main idea is to hide the indices into appropriately selected chroma and luma transform coefficients. To minimize the prediction errors, the modification is performed via a rate-distortion optimization. Objective improvements (up to 2.3% bitrate saving) and subjective assessment of chroma loss are reported and analyzed for several sequences

MODIFIED MULTI-LEVEL STEGANOGRAPHY TO ENHANCE DATA SECURITY

Data-hiding using steganography algorithm becomes an important technique to prevent unauthorized users to have access to a secret data.  In this paper, steganography algorithm has been constructed to hide a secret data in a gray and a color images, this algorithm is named deep hiding/extraction algorithm (DHEA) to modify multi-level steganography (MLS). The suggested hiding algorithm is based on modified least significant bit (MDLSB) to scatter data in a cover-image and it utilizes a number of levels; where each level perform hiding data on a gray image except the last level that applies a color image to keep secret data. Furthermore, proper randomization approach with two layers is implemented; the first layer uses random pixels selection for hiding a secret data at each level, while the second layer implements at the last level to move randomly from segment to the others. In addition, the proposed hiding algorithm implements an effective lossless image compression using DEFLATE algorithm to make it possible to hide data into a next level. Dynamic encryption algorithm based on Advanced Encryption Standard (AES) is applied at each level by changing cipher keys (Ck) from level to the next, this approach has been applied to increase the security and working against attackers. Soft computing using a meta-heuristic approach based on artificial bee colony (ABC) algorithm has been introduced to achieve smoothing on pixels of stego-image, this approach is effective to reduce the noise caused by a hidden large amount of data and to increase a stego-image quality on the last level. The experimental result demonstrates the effectiveness of the proposed algorithm with bee colony DHA-ABC to show high-performing to hide a large amount of data up to four bits per pixel (bpp) with high security in terms of hard extraction of a secret message and noise reduction of the stego-image. Moreover, using deep hiding with unlimited levels is promising to confuse attackers and to compress a deep sequence of images into one image