Practical steganalysis of digital images: state of the art

By Jessica J. Fridrich, Miroslav Goljan. IS&T/SPIE Electronic Imaging, 2002

486 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ab0bcc6366b23a7e3b0720d29737387fd63312ad

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In Computer Vision and Pattern Recognition

1. Min Wu
2. Xingming Sun
3. Xinhui Wang
4. Zhihua Xia
5. Joan V. Condell
6. Abbas Cheddad
7. Paul Mc Kevitt
8. K.J.R. Liu

Citations in the corpus

• Steganalysis of LSB matching in grayscale images (2005, background): “Finally, we outline some further directions for research.”
• Digital image steganography: Survey and analysis of current methods (2010, background): “Determining the maximal safe bit-rate that can be embedded in a given image without introducing statistical artifacts remains a very complicated task [88].”
• Steganalysis of least significant bit matching using multi-order differences (2014, background): “Generally speaking, if steganalysis algorithm can identify whether a given image contains the hidden message or not, the corresponding steganographic system is considered to be broken by this steganalysis algorithm [2,3].”
• A steganographic method for images by pixel-value differencing (2003, methodology): “Some steganalytic methods for detecting the presence of hidden information in images, such as visual attacks and statistical attacks, have also been explored in (Fridrich and Goljan, 2002; Westfeld and Pfitzmann, 2000).”
• Deep residual learning for image steganalysis (2018, background): “10 Non-bottleneck block [0, 0, 0, 0] 20 Non-bottleneck block [1, 2, 1, 1] 30 Non-bottleneck block [2, 3, 3, 2] 40 Non-bottleneck block [2, 5, 5, 3] 50 Bottleneck block [2, 3, 5, 2] 60 Non-bottleneck block [2, 8, 8, 7] [n1, n2, n3, n4] represents the number of blocks for ordinary residual learning, w”
• Improved Detection of LSB Steganography in Grayscale Images (2004, background): “For now we are not interested in more advanced analysis of suspect images such as estimates of hidden message length [4,8,5], except in as much as they function as discriminating statistics for the simple classification problem.”
• MBRS: Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression (2021, background): “The earliest research [23] encodes the secret message on the least significant bits (LSB) of the image pixels, but this method can be easily detected by statistical measures [8, 9].”
• Hiding Images in Plain Sight: Deep Steganography (2017, background): “There are many well publicized nefarious applications of steganographic information hiding, such as planning and coordinating criminal activities through hidden messages in images posted on public sites – making the communication and the recipient difficult to discover [6].”
• Data-Hiding Codes (2005, background): “, who developed a simple but surprisingly powerful algorithm (RS steganalysis) to detect the presence of hidden information in the LSB plane [134], [135].”
• Digital image forensics via intrinsic fingerprints (2008, background): “Further, the estimates are shown to be robust to moderate levels of postprocessing operations, such as JPEG compression, and white balancing done inside the cameras [33].”