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Reversible Data Hiding is a lossless data embedding technique which embeds a message code into a digital image in a reversible method. Difference Expansion (DE) is used for reversible data embedding. In this paper, a DE based scheme is used to minimize the auxiliary information being embedded and to increase the length of the watermark. The concepts discussed in this paper are: Embedding, Extraction and Restoration of the embedded data with minimal distortion. The redundancy in the image has been explored to observe reversibility. PSNR, Watermark Length and Payload Capacity are the metrics used to analyze the performance. It was concluded that by using reversible data hiding, more data could be embedded with minimal loss in information compared to other methods.

Reversible Data Hiding is a lossless data embedding technique which embeds a message code into a digital image in a reversible method. Difference Expansion (DE) is used for reversible data embedding. In this paper, a DE based scheme is used to minimize the auxiliary information being embedded and to increase the length of the watermark. The concepts discussed in this paper are: Embedding, Extraction and Restoration of the embedded data with minimal distortion. The redundancy in the image has been explored to observe reversibility. PSNR, Watermark Length and Payload Capacity are the metrics used to analyze the performance. It was concluded that by using reversible data hiding, more data could be embedded with minimal loss in information compared to other methods.

Reversible Data Hiding is a lossless data embedding technique which embeds a message code into a digital image in a reversible method. Difference Expansion (DE) is used for reversible data embedding. In this paper, a DE based scheme is used to minimize the auxiliary information being embedded and to increase the length of the watermark. The concepts discussed in this paper are: Embedding, Extraction and Restoration of the embedded data with minimal distortion. The redundancy in the image has been explored to observe reversibility. PSNR, Watermark Length and Payload Capacity are the metrics used to analyze the performance. It was concluded that by using reversible data hiding, more data could be embedded with minimal loss in information compared to other methods.

Reversible Data Hiding is a lossless data embedding technique which embeds a message code into a digital image in a reversible method. Difference Expansion (DE) is used for reversible data embedding. In this paper, a DE based scheme is used to minimize the auxiliary information being embedded and to increase the length of the watermark. The concepts discussed in this paper are: Embedding, Extraction and Restoration of the embedded data with minimal distortion. The redundancy in the image has been explored to observe reversibility. PSNR, Watermark Length and Payload Capacity are the metrics used to analyze the performance. It was concluded that by using reversible data hiding, more data could be embedded with minimal loss in information compared to other methods.

Reversible Data Hiding is a lossless data embedding technique which embeds a message code into a digital image in a reversible method. Difference Expansion (DE) is used for reversible data embedding. In this paper, a DE based scheme is used to minimize the auxiliary information being embedded and to increase the length of the watermark. The concepts discussed in this paper are: Embedding, Extraction and Restoration of the embedded data with minimal distortion. The redundancy in the image has been explored to observe reversibility. PSNR, Watermark Length and Payload Capacity are the metrics used to analyze the performance. It was concluded that by using reversible data hiding, more data could be embedded with minimal loss in information compared to other methods.

In this paper, we propose to embed messages in raw images using a recursive block partitioning technique adopted from HEVC video coding technology. This work introduces a quad-tree partitioning solution in which square blocks of pixels are partitioned recursively according to a homogeneity criterion. As such, large block sizes are allocated for homogenous areas of the image and smaller block sizes are used elsewhere. For this to work, the variable block sizes should cover the whole image without gaps. Additionally, the criterion used for determining the block partitioning should work for both message embedding and message extraction. As such, the extractor process can reproduce the same variable block sizes. Matrix encoding is used for message embedding and extraction, as such, a maximum of one pixel value is changed per block. The proposed solution is assessed in terms of percentage of changed pixels, PSNR, SSIM, histogram changes and blind steganalysis. Comparison with existing work reveals that the proposed solution reduces both the image distortions and pixel change rates. With embedding rates ranging from 2.5% to 38%, the average PSNR of the cover images ranged from 72dB to 60dB, and the average normalized histogram differences ranged from 3 to 12 pixels. It is also shown that the proposed solution is less detectible when tested with blind steganalysis in comparison to existing solutions where the average detection accuracy is 49.8%.

Steganography is the art and science of hiding information. This paper introduces e-Forensics as a novel technique for extracting secret information electronically creatively. We propose an e-Forensics system capable of detecting or extracting secret information using generic steganalytic algorithms. The system is based on an agent computing approach where the autonomic agent would traverse several websites and detect steganographic communication. If steganographic activity was detected it would report back to the concerned authority. A conceptual overview of the system, as well as its design layout is presented. An illustrative example clarifies system's functionality and performance.

This paper introduces data embedding schemes for covert communications, information augmentation, and digital watermarking. Although most digital objects may be used as a container for embedded data, this paper focuses on digital images. It discusses the data embedding framework -- the assumptions made on the communication channel, the issues considered for implementation, and the general requirements imposed on data embedding techniques. The paper presents a number of spatial and spectral data embedding techniques and touches on the general limitations these techniques face. Several application scenarios are presented and the manner in which the data embedding framework is typically tailored and applied to them is proposed.