Digital image steganography using universal distortion

By Vojtech Holub, Jessica J. Fridrich. Information Hiding and Multimedia Security Workshop, 2013

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Deep learning for steganalysis via convolutional neural networks (2015, methodology): “To evaluate the effectiveness of the developed model for steganalysis, we ran experiments on three state-ofthe-art spatial domain steganographic algorithms: HUGO,23 WOW,24 and S-UNIWARD.25 For the BOSSbase image source, we evaluated the performance of all methods on three payloads: 0.3, 0.4, and 0.5”
Universal distortion function for steganography in an arbitrary domain (2014, methodology): “This paper is an extended and adjusted version of an article presented at the First ACM Information Hiding and Multimedia Security Workshop in Montpellier in June 2013 [19].”
A new cost function for spatial image steganography (2014, methodology): “HUGO (binary embedding with parameters γ = 1, σ = 1, T = 255) [6], WOW [8], and SUNIWARD [10] are used for comparison.”
Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters (2015, methodology): “…PQ), PQe (energy-adaptive PQ) [9], MOD (Model Optimized Distortion) [4], EBS (Entropy Block Steganography) [24], UED (Uniform Embedding Distortion) [11], JUNIWARD (JPEG Universal Wavelet Relative Distortion) [12], SI-UNIWARD (Side-informed Universal Wavelet Relative Distortion) [12] and so on.”
A New Payload Partition Strategy in Color Image Steganography (2020, methodology): “In the following experiments, WOW [7], S-UNIWARD [8] and HILL [9] are combined with the proposed ACMP strategy to evaluate the performance.”