Steganalysis

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  1. His main research areas are applications of coding theory in information hiding, and machine learning techniques in steganalysis.[1]
  2. The software performs steganalysis on JPEG image files in an efficient and effective way.[2]
  3. Steganalysis is the study of detecting secret information hidden in objects such as images, videos, texts, time series and games via steganography.[3]
  4. Next, we consider statistical steganalysis of images in two different frameworks.[3]
  5. We also investigate steganalysis using the deep learning framework.[3]
  6. After applying a correcting transform to an image, statistical steganalysis is no longer able to detect the presence of steganography.[4]
  7. Steganalysis is the counter part of steganography and it is defined as the art or science of discovering hidden data in cover objects.[5]
  8. Due to the development of convolutional neural networks (CNNs), a variety of steganalysis algorithms based on CNN were proposed to enhance the efficiency of steganalysis.[6]
  9. Steganalysis is a technique to detect the trace of steganography.[6]
  10. Early steganalysis is mainly based on simple statistical features in lower dimensions.[6]
  11. The residuals extracted by the filter of SQUARE 3 × 3 are more beneficial to steganalysis.[6]
  12. In turn the development of these techniques have led to an increased interest in steganalysis techniques.[7]
  13. More specifically Universal steganalysis techniques have become more attractive since they work independently of the embedding technique.[7]
  14. These universal steganalysis techniques are tested against a number of well know embedding techniques, including Outguess, F5, Model based, and perturbed quantization.[7]
  15. We benchmark embedding rate versus detectability performances of several widely used embedding as well as universal steganalysis techniques.[7]
  16. Steganalysis is the technology that attempts to defeat steganography--by detecting the hidden information and extracting or destroying it.[8]
  17. The fact that steganography cannot be detected at all times makes steganalysis an area of ongoing research.[8]
  18. Steganalysis needs to be further developed to help counter high tech terrorism and cases of industrial espionage.[8]
  19. The performance of image steganalysis in detecting image steganography has greatly improved with the development of image steganography to more covertly and skillfully hide a message.[9]
  20. Steganography tries to hide messages in plain sight while steganalysis tries to detect their existence or even more to retrieve the embedded data.[10]
  21. Both steganography and steganalysis received a great deal of attention, especially from law enforcement.[10]
  22. Many powerful and robust methods of steganography and steganalysis have been presented in the literature over the last few years.[10]
  23. Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media using steganography.[11]
  24. Both steganography and steganalysis have received a great deal of attention from law enforcement and the media.[11]
  25. In the past years many powerful and robust methods of steganography and steganalysis have been reported in the literature.[11]
  26. In this paper, we classify and give an account of the various approaches that have been proposed for steganalysis.[11]
  27. This would increase the success rate of steganalysis by detecting data in transform domain.[12]
  28. This scheme is feature based in the sense that features that are sensitive to embedding changes are being employed as means of steganalysis.[12]
  29. This paper presents a steganalysis method for JPEG images based on Cachin criterion.[13]
  30. Experimental results show that the proposed steganalysis scheme has better performance compared to the current steganalysis methods.[13]
  31. In order to reveal the existence of hidden message, current video steganalysis divides the video into detection intervals (DI) with fixed-length and then extracts feature from every DI.[14]
  32. Although various steganalytic approaches have been presented, there are still many challenges in the field of video MVs targeted steganalysis.[14]
  33. The basic principle of the current video steganalysis is to analyze the embedding perturbation and statistical changes within the fixed-length DIs.[14]
  34. Consequently the restrictions of current video steganalysis can be concluded as follows.[14]

소스

  1. ↑ Machine Learning in Image Steganalysis
  2. ↑ Annual ADFSL Conference on Digital Forensics, Security and Law: CANVASS
  3. ↑ 3.0 3.1 3.2 Statistical Steganalysis of Images
  4. ↑ Defending Against Statistical Steganalysis
  5. ↑ Steganalysis, the Counterpart of Steganography
  6. ↑ 6.0 6.1 6.2 6.3 IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel
  7. ↑ 7.0 7.1 7.2 7.3 Benchmarking steganographic and steganalysis techniques
  8. ↑ 8.0 8.1 8.2 Steganalysis
  9. ↑ CNN-Based Ternary Classification for Image Steganalysis
  10. ↑ 10.0 10.1 10.2 A review of image steganalysis techniques for digital forensics
  11. ↑ 11.0 11.1 11.2 11.3 Classification of steganalysis techniques: A study
  12. ↑ 12.0 12.1 Steganalysis for Calibrated and Lower Embedded Uncalibrated Images
  13. ↑ 13.0 13.1 Steganalysis for JPEG Images Based on Statistical Features of Stego and Cover Images
  14. ↑ 14.0 14.1 14.2 14.3 Segmentation Based Video Steganalysis to Detect Motion Vector Modification

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