New Design Paradigm of Distortion Cost Function for Efficient JPEG Steganography

TL;DR

This paper introduces a new paradigm for JPEG steganography that optimizes distortion costs in the spatial domain using a generalized transformation, enhancing undetectability against phase-aware steganalysis.

Contribution

The paper proposes a novel distortion cost domain transformation (DCDT) that improves embedding efficiency and undetectability in JPEG steganography, outperforming existing methods.

Findings

DCDT with HiLL outperforms state-of-the-art schemes in resisting JPEG phase-aware steganalysis.

The proposed method has lower computational complexity than BET-HiLL.

Effective across various image databases and quality factors.

Abstract

Recently, with the introduction of JPEG phase-aware steganalysis features, e.g., GFR, the design of JPEG steganographic distortion cost function turns to maintain not only the statistical undetectability in DCT domain but also in spatial domain. To tackle this issue, this paper presents a novel paradigm for the design of JPEG steganographic distortion cost function, which calculates the distortion cost via a generalized Distortion Cost Domain Transformation (DCDT) function. The proposed function comprises the decompressed pixel block embedding changes and their corresponding embedding distortion costs for unit change, where the pixel embedding distortion costs are represented in a more general exponential model, aiming to flexibly allocate the embedding data. In this way, the JPEG steganography could be formulated as the optimization problem of minimizing the overall distortion cost in its decompressed spatial domain, which is equivalent to maximizing its statistical undetectability against JPEG phase-aware steganalysis features. Experimental results show that the proposed DCDT equipped with HiLL (a spatial steganographic distortion cost function) is superior to other state-of-the-art JPEG steganographic schemes, e.g., UERD, J-UNIWARD, and GUED in resisting the detection of JPEG phase-aware feature-based steganalyzers GFR and SCA-GFR, and rivals BET-HiLL with one order of magnitude lower computational complexity, along with the possibility of being further improved by considering the mutually dependent embedding interactions. In addition, the proposed DCDT is also verified to be effective for different image databases and quality factors.