A Markov Process Based Approach to Effective Attacking JPEG Steganography

By Y. Shi, Chunhua Chen, Wen Chen. Information Hiding, 2006

418 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/dd54df0ff17fe9e942c3605df67700538a4d52fe

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In Computer Vision and Pattern Recognition

Citations in the corpus

Benchmarking for Steganography (2008, background): “There are basically two choices: 1) analytical models, in which c is modeled as a sequence of iid random variables [28] (or Markov chains [39]) and 2) high-dimensional models based on features extracted from the cover/stego objects [32, 13, 2, 1, 43, 36, 26, 11].”
Ensemble Classifiers for Steganalysis of Digital Media (2012, methodology): “The feature set designed for JPEG images described in [42] used 274 features and was later extended to twice its size [28] by Cartesian calibration, while 324-and 486-dimensional feature vectors were proposed in [48] and [11], respectively.”
Merging Markov and DCT features for multi-class JPEG steganalysis (2007, methodology): “Recently, Shi et al. 21 proposed another feature set for JPEG images based on Markov models of DCT plane.”
A Survey on Image Steganography and Steganalysis (2011, methodology): “Inspired by Sullivan et al.’s work, Shi et al. [38] proposed a Markov process based method that explores the intra-block DCT dependency for steganalyzing JPEG steganography blindly.”
Rich Models for Steganalysis of Digital Images (2012, background): “The model is usually determined not only by the characteristics of the cover source but also by the effects of embedding [4], [6], [14], [18], [21], [33], [35], [38].”
Deep Residual Network for Steganalysis of Digital Images (2019, background): “[1], [2] and Farid and Siwei [18] in early 2000’s and was later greatly improved by representing images with higher-order statistics of noise residuals or DCT coefficients [43], [49], [68].”
An improved approach to steganalysis of JPEG images (2010, methodology): “First, a recently proposed Markov approach [27] is expanded to the inter-block of the discrete cosine transform (DCT) and to the discrete wavelet transform (DWT).”
Steganalysis of JPEG images using rich models (2012, background): “Depending on the mutual position of the DCT modes ( x, y ) and ( x + ∆ x, y + ∆ y ), the extracted co-occurrence matrices C ∈ { ¯ C × T , ˆ C → T , ˆ C T , ˆ C ⇒ T } will be grouped into ten qualitatively diﬀerent submodels: The ﬁrst six submodels capture intra-block relationships: G h –…”
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities (2007, methodology): “These claims are supported by the results reported in [33] and independently in [29].”
Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT (2015, methodology): “Selected examples of popular feature sets proposed for detection of steganography in JPEG images are the historically first image quality metric features [1], first-order statistics of wavelet coefficients [8], Markov features formed by sample intra-block conditional probabilities [29], inter- and i”