Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited

Abstract

Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.

Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

“…From early HUGO [2], to latest HILL [3] and MiPOD [4], the past few years witnessed the flourish of additive schemes in spatial domain. In JPEG domain, UED [5] and UERD [6] are two additive schemes with good security performance. UNIWARD proposed in [7] is an additive distortion function which can be applied for embedding both in spatial and JPEG domains.…”

“…Its JPEG version, J-UNIWARD, achieves This work was supported in part by the NSFC (61772349, U1636202, 61402295, 61572329, 61702340), Guangdong NSF (2014A030313557), Shenzhen R&D Program (JCYJ20160328144421330). This work was also supported by Alibaba Group through Alibaba Innovative Research (AIR) Pro best performance [6], [7]. Research on non-additive distortion functions has made great progress in the spatial domain [8], [9].…”

“…However, analogous schemes have not yet been proposed in the JPEG domain. Although utilizing side information of a pre-cover image (raw or uncompressed) can improve the security of JPEG steganography [6], [7], [10], its applicability remains limited due to scarce availability of pre-cover images.…”

IStego100K: Large-Scale Image Steganalysis Dataset

“…It contains 208,104 images with the same size of 1024*1024, of which 200,000 images (100,000 cover-stego image pairs) construct the training set and the remaining 8,104 are as testing sets. Each steganographic image is randomly steganized with three widely used image steganography (J-uniward [20], NSF5 [21] and UERD [22]) with a random embedding rate (0.1-0.4). At the same time, we also choose some latest steganalysis algorithms to test IStego100K dataset.…”

Lost in the Digital Wild

“…Traditional information hiding schemes embed hidden data into a cover object (e.g., a file or a communication channel) by slightly modifying its content, and hope to minimize the distortion between the original cover object and its information-bearing version [6,9,11,22,23,37,42]. When an information hiding method is used for security purposes, it is commonly called steganography where the threat model is that the communications between the sender and the receiver are under control of untrusted third-parties like hostile authorities.…”