Shunquan Tan

A Strategy of Clustering Modification Directions in Spatial Image Steganography

Most of the recently proposed steganographic schemes are based on minimizing an additive distortion function defined as the sum of embedding costs for individual pixels. In such an approach, mutual embedding impacts are often ignored. In this paper, we present an approach that can exploit the interactions among embedding changes in order to reduce the risk of detection by steganalysis. It employs a novel strategy, called clustering modification directions (CMDs), based on the assumption that when embedding modifications in heavily textured regions are locally heading toward the same direction, the steganographic security might be improved. To implement the strategy, a cover image is decomposed into several subimages, in which message segments are embedded with well-known schemes using additive distortion functions. The costs of pixels are updated dynamically to take mutual embedding impacts into account. Specifically, when neighboring pixels are changed toward a positive/negative direction, the cost of the considered pixel is biased toward the same direction. Experimental results show that our proposed CMD strategy, incorporated into existing steganographic schemes, can effectively overcome the challenges posed by the modern steganalyzers with high-dimensional features.

Investigation on Cost Assignment in Spatial Image Steganography

Relating the embedding cost in a distortion function to statistical detectability is an open vital problem in modern steganography. In this paper, we take one step forward by formulating the process of cost assignment into two phases: 1) determining a priority profile and 2) specifying a cost-value distribution. We analytically show that the cost-value distribution determines the change rate of cover elements. Furthermore, when the cost-values are specified to follow a uniform distribution, the change rate has a linear relation with the payload, which is a rare property for content-adaptive steganography. In addition, we propose some rules for ranking the priority profile for spatial images. Following such rules, we propose a five-step cost assignment scheme. Previous steganographic schemes, such as HUGO, WOW, S-UNIWARD, and MG, can be integrated into our scheme. Experimental results demonstrate that the proposed scheme is capable of better resisting steganalysis equipped with high-dimensional rich model features.

Stacked convolutional auto-encoders for steganalysis of digital images

In this paper, we point out that SRM (Spatial-domain Rich Model), the most successful steganalysis framework of digital images possesses a similar architecture to CNN (convolutional neural network). The reasonable expectation is that the steganalysis performance of a well-trained CNN should be comparable to or even better than that of the hand-coded SRM. However, a CNN without pre-training always get stuck at local plateaus or even diverge which result in rather poor solutions. In order to circumvent this obstacle, we use convolutional auto-encoder in the pre-training procedure. A stack of convolutional auto-encoders forms a CNN. The experimental results show that initializing a CNN with the mixture of the filters from a trained stack of convolutional auto-encoders and feature pooling layers, although still can not compete with SRM, yields superior performance compared to traditional CNN for the detection of HUGO generated stego images in BOSSBase image database.

Targeted Steganalysis of Edge Adaptive Image Steganography Based on LSB Matching Revisited Using B-Spline Fitting

In this letter, the authors point out that the readjusting phase of edge adaptive image steganography based on LSB matching revisited introduces a pulse distortion to the long exponential tail of the histogram of the absolute difference of the pixel pairs. Making use of this observation, a targeted steganalytic method based on B-Spline fitting is proposed. Experimental results show that the proposed method obtains excellent results for detecting stego images with low embedding rate. The dominant performance of our method compared with state-of-the-art blind steganalyzers, such as SPAM and SRM is apparent. Furthermore, our method can accurately estimate the threshold used in the secret data embedding procedure and can separate the stego images with unit block size from those with block sizes greater than one.

Revealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis

To identify whether an image has been JPEG compressed is an important issue in forensic practice. The state-of-the-art methods fail to identify high-quality compressed images, which are common on the Internet. In this paper, we provide a novel quantization noise-based solution to reveal the traces of JPEG compression. Based on the analysis of noises in multiple-cycle JPEG compression, we define a quantity called forward quantization noise. We analytically derive that a decompressed JPEG image has a lower variance of forward quantization noise than its uncompressed counterpart. With the conclusion, we develop a simple yet very effective detection algorithm to identify decompressed JPEG images. We show that our method outperforms the state-of-the-art methods by a large margin especially for high-quality compressed images through extensive experiments on various sources of images. We also demonstrate that the proposed method is robust to small image size and chroma subsampling. The proposed algorithm can be applied in some practical applications, such as Internet image classification and forgery detection.

Automatic Detection of Object-Based Forgery in Advanced Video

Passive multimedia forensics has become an active topic in recent years. However, less attention has been paid to video forensics. Research on video forensics, and especially on automatic detection of object-based video forgery, is still in its infancy. In this paper, we develop an approach for automatic identification and forged segment localization of object-based forged video encoded with advanced frameworks. The proposed approach starts with a frame manipulation detector. An automatic algorithm is proposed to identify object-based video forgery based on the frame manipulation detector. Then, a two-stage automatic algorithm is provided to accurately locate the forged video segments in the suspicious video. To construct the proposed frame manipulation detector, motion residuals are generated from the target video frame sequence. We regard the object-based forgery in video frames as image tampering in the motion residuals and employ the feature extractors that are originally built for still image steganalysis to extract forensic features from the motion residuals. The experiments show that the proposed approach achieves excellent results in both forged video identification and automatic forged temporal segment localization.

Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a steganalytic discriminative subnetwork. Via alternately training these two oppositional subnetworks, our proposed framework can automatically learn embedding change probabilities for every pixel in a given spatial cover image. The learnt embedding change probabilities can then be converted to embedding distortions, which can be adopted in the existing framework of minimal-distortion embedding. Under this framework, the distortion function is directly related to the undetectability against the oppositional evolving steganalyzer. Experimental results show that with adversarial learning, our proposed framework can effectively evolve from nearly naïve random

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

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Statistical Model of JPEG Noises and Its Application in Quantization Step Estimation

embedding at the beginning to much more advanced content-adaptive embedding which tries to embed secret bits in textural regions. The security performance is also steadily improved with increasing training iterations.

Pixel-Decimation-Assisted Steganalysis of Synchronize-Embedding-Changes Steganography

Adoption of deep learning in image steganalysis is still in its initial stage. In this paper, we propose a generic hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. Our proposed framework involves two main stages. The first stage is hand-crafted, corresponding to the convolution phase and the quantization and truncation phase of the rich models. The second stage is a compound deep-neural network containing multiple deep subnets, in which the model parameters are learned in the training procedure. We provided experimental evidence and theoretical reflections to argue that the introduction of threshold quantizers, though disabling the gradient-descent-based learning of the bottom convolution phase, is indeed cost-effective. We have conducted extensive experiments on a large-scale data set extracted from ImageNet. The primary data set used in our experiments contains 500 000 cover images, while our largest data set contains five million cover images. Our experiments show that the integration of quantization and truncation into deep-learning steganalyzers do boost the detection performance by a clear margin. Furthermore, we demonstrate that our framework is insensitive to JPEG blocking artifact alterations, and the learned model can be easily transferred to a different attacking target and even a different data set. These properties are of critical importance in practical applications.