In this paper, a parallel scheme for self-recovery of tampered images and videos is proposed. Designed technique is based on two methods for generating the digest image: halftoning and a block based scheme, additionally, the implementation of an authentication algorithm was carried out using a block-based method. In order to obtain robustness to the tampering coincidence problem, the proposed scheme embeds multiple versions of the recovery watermark. Finally, during the recovery process, an algorithm of inverse halftoning was applied to improve the quality of the reconstructed image. Proposed scheme was implemented in such a way that each process should be highly parallelizable using GPU, multicore processors. Experimental results have shown that novel framework generates a good quality of the watermarked images and the recovered images. The simulation results using parallel architectures have demonstrated the efficiency of the novel technique when it is implemented in a real-time environment.
In this paper, a novel approach for halftone images is proposed and implemented for images that are obtained by the Dot Diffusion (DD) method. Designed technique is based on an optimization of the so-called class matrix used in DD algorithm and it consists of generation new versions of class matrix, which has no baron and near-baron in order to minimize inconsistencies during the distribution of the error. Proposed class matrix has different properties and each is designed for two different applications: applications where the inverse-halftoning is necessary, and applications where this method is not required. The proposed method has been implemented in GPU (NVIDIA GeForce GTX 750 Ti), multicore processors (AMD FX(tm)-6300 Six-Core Processor and in Intel core i5-4200U), using CUDA and OpenCV over a PC with linux. Experimental results have shown that novel framework generates a good quality of the halftone images and the inverse halftone images obtained. The simulation results using parallel architectures have demonstrated the efficiency of the novel technique when it is implemented in real-time processing.
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