Detecting LSB Steganography in Color and Gray-Scale Images

By Jessica J. Fridrich, Miroslav Goljan, Rui Du. IEEE Multim., 2001

977 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c0f284378f906cb4531ee1d2047895be376486f5

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In Computer Vision and Pattern Recognition

Citations in the corpus

Edge Adaptive Image Steganography Based on LSB Matching Revisited (2010, methodology): “As a result, some structural asymmetry (never decreasing even pixels and increasing odd pixels when hiding the data) is introduced, and thus it is very easy to detect the existence of hidden message even at a low embedding rate using some reported steganalytic algorithms, such as the Chi-squared att”
Hiding Images in Plain Sight: Deep Steganography (2017, background): “The most common steganography approaches manipulate the least significant bits (LSB) of images to place the secret information - whether done uniformly or adaptively, through simple replacement or through more advanced schemes [9, 10].”
Image Steganography and Steganalysis: Concepts and Practice (2003, background): “in [14] that had first provided remarkable detection accuracy and message length estimation even for short messages.”
Steganalysis of JPEG Images: Breaking the F5 Algorithm (2002, methodology): “Fridrich et al. [5, 6 ] introduced the dual statistics steganalytic method for detection of LSB embedding in uncompressed formats.”
HiNet: Deep Image Hiding by Invertible Network (2021, methodology): “Image hiding in pixel domain can easily lead to texture-copying artifacts and color distortion [11, 32].”
Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis (2020, methodology): “The existing steganalysis methods are usually classified into specific steganalysis algorithms [5], [48], [49] and universal steganalysis algorithms [13], [15], [44].”
Generating steganographic images via adversarial training (2017, methodology): “However, information from the original image is always lost when using the LSB technique, and is known to be vulnerable to steganalysis [6].”
LSB matching revisited (2006, methodology): “Due to imbalance in the embedding distortion, LSB replacement can be detected by the current detection methods [2] - [6].”
Digital image steganography: Survey and analysis of current methods (2010, methodology): “An image used to test for the RS steganalysis’ performance [112].”
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].”