Model-Based Steganography

By Phil Sallee. International Workshop on Digital Watermarking, 2003

507 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4dcf937953b680f2a6718690e52b78ae4caeb8dd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jessica J. Fridrich
2. Vojtech Holub
3. Bin Li
4. Jan Kodovský
5. Jiwu Huang
6. Tomás Pevný
7. Y. Shi
8. Patrick Bas

Citations in the corpus

• Benchmarking for Steganography (2008, background): “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: Survey and analysis of current methods (2010, methodology): “It is also known as ‘‘Statistics-aware embedding’’ [3], ‘‘Masking’’ [1] or ‘‘Model-Based’’ [58].”
• An improved approach to steganalysis of JPEG images (2010, methodology): “Model Based steganography without deblocking (MB1): Sallee presented an information-theoretic method for producing steganography [24].”
• 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].”
• A Survey on Image Steganography and Steganalysis (2011, methodology): “Sallee[32] presented a general framework for performing steganography and steganalysis using a statistical model of the cover media.”
• Detection of Double-Compression in JPEG Images for Applications in Steganography (2008, methodology): “Because we need to test the performance of the DC detector also on single-compressed images to evaluate its false positive rate, we also prepared single-compressed images with quality factors 75 and 80 embedded by the following steganographic algorithms: F5 [18], Model Based Steganography without [1”
• Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters (2015, unknown): “The former include Jsteg, MB1 [22], M-B2, Outguess [21], F5 [25], nsF5 [9], MME (modified matrix encoding) [16], PQ (perturbed quantization) [7], etc.”
• Ensemble Classifiers for Steganalysis of Digital Media (2012, methodology): “We show a specific example of how one can build a diverse high-dimensional feature space for analysis of JPEG images and readily use it to markedly improve the detection of nsF5, YASS, and MBS.”
• Statistically undetectable jpeg steganography: dead ends challenges, and opportunities (2007, methodology): “html without deblocking [31], MMx [21], and Perturbed Quantization while double compressing [16].”
• Feature-Based Steganalysis for JPEG Images and Its Implications for Future Design of Steganographic Schemes (2004, methodology): “In Section 3, we give the details of the detection scheme and discuss the experimental results for OutGuess [11], F5 [13], and Model Based Steganography [12,14].”
• A New Payload Partition Strategy in Color Image Steganography (2020, methodology): “Inspired by the model-based steganography [11], MG (multivariate Gaussian) [12] is a model-driven framework to obtain the costs.”
• Steganalysis by Subtractive Pixel Adjacency Matrix (2009, background): “The reported errors (2), measured on images from the testing set, are intra-database errors, which means that the images in the training and testing set came from the same database.”