Statistically undetectable jpeg steganography: dead ends challenges, and opportunities

By Jessica J. Fridrich, Tomás Pevný, Jan Kodovský. Workshop on Multimedia & Security, 2007

448 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c5e7fcd75e433a2a5a6f3f779feb09cceb0457d2

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey on Image Steganography and Steganalysis (2011, background): “[81, 82], minimizing the embedding distortions does make the steganography less detectable.”
Digital image steganography: Survey and analysis of current methods (2010, background): “A shrinkage occurs, as described in [50], when the same bit has to be re-embedded in case the original coefficient is either ‘1’ or ‘-1’ as at the decoding phase all zero coefficients will be skipped whether they were modified or not.”
StegaStamp: Invisible Hyperlinks in Physical Photographs (2019, methodology): “Often methods are designed to evade steganalysis, the detection of hidden messages [18, 34].”
Universal distortion function for steganography in an arbitrary domain (2014, methodology): “Currently, the most secure embedding method for JPEG images that does not use any side information is the uniform embedding distortion (UED) [13] that substantially improved upon the nsF5 algorithm [14] - the previous state of the art.”
Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes (2011, methodology): “The boundedness of is not limiting the sender in practice since the case when a particular value is forbidden (a requirement often found in practical steganographic schemes [16]) can be resolved by excluding from .”
Ensemble Classifiers for Steganalysis of Digital Media (2012, methodology): “In our effort to provide an example of usage in practice , we introduce a compact general-purpose feature set for the DCT domain and use it to improve detection of nsF5 [20], model-based steganography (MBS) [43], and YASS, three representatives of different embedding paradigms.”
Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT (2015, methodology): “Figures 4, 5 and 6 show the detection error EOOB for J-UNIWARD [14], ternary-coded UED (Uniform Embedding Distortion) [12], and nsF5 [11] achieved using the proposed DCTR, the JPEG Rich Model (JRM) [20] of dimension 22,510, the 12,753-dimensional version of the Spatial Rich Model called SRMQ1 [10], ”
Steganalysis of JPEG images using rich models (2012, methodology): “In Section 3, it is used to steganalyze six modern JPEG-domain steganographic schemes: nsF5 [5], MBS [19], YASS [21], MME [6], BCH, and BCHopt [18].”
Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters (2015, methodology): “The former include Jsteg, MB1 [22], M-B2, Outguess [21], F5 [25], nsF5 [9], MME (modiﬁed matrix encoding) [16], PQ (perturbed quantization) [7], etc.”
Benchmarking for Steganography (2008, methodology): “The tested stego algorithms include F5 [42], –F5 [16], F5 without shrinkage [16] (nsF5), JP Hide&Seek(8), Model Based Steganography without deblocking [34] (MBS1), MMx [25], Steghide [19], Perturbed Quantization while double compressing [15](PQ) and its two modifications (PQt and PQe) as described i”
Digital image steganography using universal distortion (2013, background): “It offers a substantially better empirical security than the nsF5 algorithm [12].”