Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes

By Tomáš Filler, Jan Judas, Jessica J. Fridrich. IEEE Transactions on Information Forensics and Security, 2011

952 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3cc55137800fac588bd0e0c71609c3afd1b2b05a

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Citations in the corpus

Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters (2015, methodology): “Compared with the non-adaptive JPEG steganography, the adaptive JPEG steganography can achieve better anti-detection abilities because the embedding changes are constrained to complicated image texture and edge regions.”
A Simple Baseline for StyleGAN Inversion (2021, methodology): “…method [13] to get the stego image, and then sends the stego image out; 3)After receiving the stego image, the recipient recovers W s accurately using the corresponding extraction algorithm [13]; 4) The recipient feeds W s to the same pre-trained StyleGAN for recovering the secret image.”
Rich Models for Steganalysis of Digital Images (2012, neutral): “While the latter may rightfully seem rather out-of-reach due to the fact that steganography can be designed to minimize the disturbance in a fixed model space using the Feature-Correction Method (FCM) [8], [26] or the framework described in [10], [12], model preservation will likely become increasin”
E2Style: Improve the Efficiency and Effectiveness of StyleGAN Inversion (2021, methodology): “2) The sender selects a carrier image, hides Was in the carrier image using the traditional steganography method [71] to get the stego image Ics, and then sends the stego image out.”
Digital image steganography using universal distortion (2013, methodology): “be able to embed in practice near the payload–distortion bound using Syndrome–Trellis Codes (STCs) [5].”
Steganalysis of JPEG images using rich models (2012, methodology): “This could be easily remedied by using more sophisticated coding schemes [2].”
A New Payload Partition Strategy in Color Image Steganography (2020, background): “Since the advanced syndrome-trellis codes (STC) [5] performs well in minimizing the additive distortion costs, researchers have been paying close attention to designing distortion functions in recent years.”
Universal distortion function for steganography in an arbitrary domain (2014, methodology): “Practical embedding algorithms can be constructed using the ternary multi-layered version of STCs (Section 4 in [3]).”
A new cost function for spatial image steganography (2014, methodology): “For example, STC (syndrometrellis code) [12] is commonly used.”
"Break Our Steganographic System": The Ins and Outs of Organizing BOSS (2011, methodology): “Due to recent development in coding techniques [2,3], we believe that larger gains (in secure payload for example) can be achieved by designing distortion functions more adaptively to the image content instead of by changing the coder.”
Automatic Steganographic Distortion Learning Using a Generative Adversarial Network (2017, methodology): “a suitably defined additive distortion function [25]”
Selection-channel-aware rich model for Steganalysis of digital images (2014, background): “The real boom of adaptive schemes started with the advancement of coding schemes [9] capable of embedding messages while nearly optimally minimizing arbitrarily defined additive distortion functions.”