Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional...
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ISBN:
(数字)9798350354171
ISBN:
(纸本)9798350354188
Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional means, ensuring that only intended recipients can decipher the hidden message. The LSB technique has attracted significant attention in the literature due to its capability to conceal secret message bits within the least significant parts of image pixels. Although LSB has performed well in image steganography, there is some way to go in terms of its development, especially in the ways in which messages are dealt with. This paper presents a new technique of image steganography basing on the LSB method and the BROTLI algorithm. It uses the compression algorithm to compress message text. After that, to scramble the text, one LSB embedding technique is then used to hide the compressed text in the cover image. Applying the methods of data analysis on the benchmark images, the new approach developed outperforms the standard methods that exist at the present stage. This supports the idea of performing BROTLI on data with a view of compressing the data to be embedded in LSBs.
Most of the existing imageencryption methods based on the region of interest (ROI) use traditional methods to manually screen regions, which are notoriously difficult to implement serialized and cannot handle multipl...
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ISBN:
(纸本)9781665409858
Most of the existing imageencryption methods based on the region of interest (ROI) use traditional methods to manually screen regions, which are notoriously difficult to implement serialized and cannot handle multiple ROI well. In addition, the low dimensional Logistic map widely used has the shortcoming of small key space and poor security. Furthermore, DNA computing, which is popular in current research, also has problems such as fixed rules and low sensitivity. Therefore, this paper improves the advanced deep learning object detection algorithm YOLOV4 tiny to process multiple ROI more quickly and accurately and then designs an elegant encryption strategy for multiple images and multiple ROI combining Logistic map, Chen system, and DNA computing. Specifically, we calculate the initial value of the Logistic map from the multiple ROI, which significantly increases the keyspace and key sensitivity. At the same time, we utilize the Chen system to design a unique DNA coding and computing rule for each ROI dynamically to improve the pixel-level scrambling and diffusion. Finally, we combine multiple images into a larger picture, and use SHA256 to calculate its hash value as the initial value to generate a chaotic sequence to complete row-column scrambling, and realize serialized multiple images encryption. Simulation experiments and security analysis results indicate that our method has good encryption and decryption effects, as well as high security and robustness, and can resist statistical attacks and brute force attacks.
The (ab)use of encryption and compression in hiding illegal digital content complicates efforts by law enforcement agencies (LEAs) to procure evidence to support the elements of proof required in criminal prosecution....
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ISBN:
(数字)9798331506209
ISBN:
(纸本)9798331506216
The (ab)use of encryption and compression in hiding illegal digital content complicates efforts by law enforcement agencies (LEAs) to procure evidence to support the elements of proof required in criminal prosecution. This reinforces the importance of designing solutions to determine the file type of an encrypted file (e.g., videos and still images in the context of illegal picture investigations), which can be used to build probable cause in a court order application to have the file decrypted. While machine learning (ML) has shown immense capabilities in several detection tasks, the suitability of ML for detecting file types in encrypted or compressed files has not been explored. Furthermore, since detecting file types in real-world LEA applications is a high-stake decision-making problem, existing ML techniques that do not provide prediction uncertainty are not as useful. In this work, we take the first step toward detecting file types in encrypted or compressed files using ML for LEA applications based on their Byte Frequency Distributions (BFD). We then compose a dataset 1 of BFDs of 300,000 encrypted and compressed data from 12,000 diverse files for five different file types. We conduct an in-depth analysis of our dataset and demonstrate the utility of ML techniques in detecting file types of content in encrypted and compressed files based on BFDs. Informed by these findings, we present our proposed framework, eDefender, designed to facilitate the detection of file types in encrypted and compressed files for LEA applications, by employing uncertainty quantification of detection scores based on ensembling. eDefender successfully flags directories with encrypted or compressed image/video-type files with an F1-score of 90.7%.
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