Guo-Shiang Lin

A feature-based classification technique for blind image steganalysis

In contrast to steganography, steganalysis is focused on detecting (the main goal of this research), tracking, extracting, and modifying secret messages transmitted through a covert channel. In this paper, a feature classification technique, based on the analysis of two statistical properties in the spatial and DCT domains, is proposed to blindly (i.e., without knowledge of the steganographic schemes) to determine the existence of hidden messages in an image. To be effective in class separation, the nonlinear neural classifier was adopted. For evaluation, a database composed of 2088 plain and stego images (generated by using six different embedding schemes) was established. Based on this database, extensive experiments were conducted to prove the feasibility and diversity of our proposed system. It was found that the proposed system consists of: 1) a 90%/sup +/ positive-detection rate; 2) not limited to the detection of a particular steganographic scheme; 3) capable of detecting stego images with an embedding rate as low as 0.01 bpp; and 4) considering the test of plain images incurred low-pass filtering, sharpening, and JPEG compression.

Public-key-based optical image cryptosystem based on data embedding techniques

We propose a public-key-based optical image cryptosystem for practical secure communications since conventional optical crypto- systems that use a symmetric algorithm are confronted by the problem of key delivery. The proposed system employs a hybrid architecture in which a double-random-phase encoding is used to cipher and decipher an image and an asymmetric algorithm is used for ciphering and deci- phering the session key. To solve this problem of key delivery, the double-random-phase encoding algorithm is analyzed to establish a co- vert channel and therefore elucidate the phenomenon that the amplitude part of the ciphered image is less sensitive to quantization error than the other parts in the Fourier and output planes. The session key is then hidden in the covert channel setup in the ciphered image and extracted at the receiver side. Experimental results demonstrate that the amplitude parts in the Fourier and output planes are better suited to convey the session key because they enable reconstructed images with a higher visual quality to be obtained.

Robust image watermarking on the DCT domain

This paper proposes two simple DCT-domain-based schemes to embed single or multiple watermarks into an image for copyright protection and data monitoring and tracking. The watermark data are essentially embedded in the middle band of the DCT domain to make a tradeoff between visual degradation and robustness. The proposed schemes are simple and no original host image is required for watermark extraction. The algorithm also features the capability of embedding multiple orthogonal watermarks into an image simultaneously. A set of systematic experiments, including Gaussian smoothing, JPEG compression, and image cropping are performed to prove the robustness of our algorithms.

Dual protection of JPEG images based on informed embedding and two-stage watermark extraction techniques

In this paper, the authors propose a watermarking scheme that embeds both image-dependent and fixed-part marks for dual protection (content authentication and copyright claim) of JPEG images. To achieve the goals of efficiency, imperceptibility, and robustness, a compressed-domain informed embedding algorithm, which incorporates the Lagrangian multiplier optimization approach and an adjustment procedure, is developed. A two-stage watermark extraction procedure is devised to achieve the functionality of dual protection. In the first stage, the semifragile watermark in each local channel is extracted for content authentication. Then, in the second stage, a weighted soft-decision decoder, which weights the signal detected in each channel according to the estimated channel condition, is used to improve the recovery rate of the fixed-part watermark for copyright protection. The experiment results manifest that the proposed scheme not only achieve dual protection of the image content, but also maintain higher visual quality (an average of 6.69 dB better than a comparable approach) for a specified level of watermark robustness. In addition, the overall computing load is low enough to be practical in real-time applications

A Passive-Blind Forgery Detection Scheme Based on Content-Adaptive Quantization Table Estimation

In this paper, we propose a passive-blind scheme for detecting forged images. The scheme leverages quantization table estimation to measure the inconsistency among images. To improve the accuracy of the estimation process, each AC DCT coefficient is first classified into a specific type; then the corresponding quantization step size is measured adaptively from its energy density spectrum (EDS) and the EDSs Fourier transform. The proposed content-adaptive quantization table estimation scheme is comprised of three phases: pre-screening, candidate region selection, and tampered region identification. In the pre-screening phase, we determine whether an input image has been JPEG compressed, and count the number of quantization steps whose size is equal to one. To select candidate regions for estimating the quantization table, we devise a candidate region selection algorithm based on seed region generation and region growing. First, the seed region generation operation finds a suitable region by removing suspect regions, after which the selected seed region is merged with other suitable regions to form a candidate region. To avoid merging suspect regions, a candidate region refinement operation is performed in the region growing step. After estimating the quantization table from the candidate region, an maximum-likelihood-ratio classifier exploits the inconsistency of the quantization table to identify tampered regions block by block. To evaluate the schemes performance in terms of tampering detection, three common forgery techniques, copy-paste tampering, inpainting, and composite tampering, are used. Experiment results demonstrate that the proposed scheme can estimate quantization tables and identify tampered regions effectively.

A Framework of Enhancing Image Steganography With Picture Quality Optimization and Anti-Steganalysis Based on Simulated Annealing Algorithm

Picture quality and statistical undetectability are two key issues related to steganography techniques. In this paper, we propose a closed-loop computing framework that iteratively searches proper modifications of pixels/coefficients to enhance a base steganographic scheme with optimized picture quality and higher anti-steganalysis capability. To achieve this goal, an anti-steganalysis tester and an embedding controller-based on the simulated annealing (SA) algorithm with a proper cost function-are incorporated into the processing loop to conduct the convergence of searches. The cost function integrates several performance indices, namely, the mean square error, the human visual system (HVS) deviation, and the differences in statistical features, and guides a proper direction of searches during SA optimization. Our proposed framework is suitable for the kind of steganographic schemes that spreads each message information into multiple pixels/coefficients. We have selected two base steganographic schemes for implementation to show the applicability of the proposed framework. Experiment results show that the base schemes can be enhanced with better performances in image PSNR (by more than 5.0 dB), file-size variation, and anti-steganalysis pass-rate (by about 10% ~ 86%, at middle to high embedding capacities).

Error Resilient Coding Based on Reversible Data Embedding Technique for H. 264/AVC Video

In this paper, a prescription-based error concealment (PEC) method is proposed. PEC relies on pre-analyses of the concealment error image (CEI) for I-frames and the optimal error concealment scheme for P-frames at encoder side. CEI is used to enhance the image quality at decoder side after error concealment (by spatial interpolation or zero motion) of the corrupted intra-coded MBs. A set of pre-selected error concealment methods is evaluated for each corrupted inter-coded MB to determine the optimal one for the decoder. Both the CEI and the scheme indices are considered as the prescriptions for decoder and transmitted along with the video bit stream based on a reversible data embedding technique. Experiments show that the proposed method is capable of achieving PSNR improvement of up to 1.48 dB, at a considerable bit-rate, when the packet loss rate is 20%

DETECTION OF FRAME DUPLICATION FORGERY IN VIDEOS BASED ON SPATIAL AND TEMPORAL ANALYSIS

In this paper, we present a passive-blind scheme for detection of frame duplication forgery in videos. The scheme is a coarse-to-fine approach that is implemented in four stages: candidate segment selection, spatial similarity measurement, frame duplication classification, and post-processing. To screen and select duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in the RGB color space is adopted as a feature. Then, to evaluate the similarity of two images, we use a block-based algorithm to measure the spatial correlation between the candidate segment and the corresponding frame in the query template. Based on the results of spatial and temporal analysis, we construct a classifier to detect duplicated clips. In addition, to deal with the partial detection problem, we develop a post-processing technique that examines and merges two adjacent detected candidates into a complete duplicated video clip. Our experiment results demonstrate that the proposed scheme can not only achieve detection of frame duplication forgery but also accurately detect and localize duplicated clips in different kinds of videos. The results also show that the scheme outperforms an existing method in terms of precision, recall, accuracy, and computation time.

Data hiding domain classification for blind image steganalysis

A statistical feature-based scheme is proposed to identify the data hiding domain of an embedded signal in this research. Two phenomena are observed for images before and after data hiding. First, the gradient energy increases as the continuity of gray levels between adjacent pixels is disturbed by the embedded signal. Second, the statistical variance of the coefficient distribution in macro-blocks tends to decrease after data hiding. These phenomena are analyzed mathematically. Then, statistical features in the pixel, DCT, and DWT domains are extracted and a maximum likelihood ratio test is adopted to solve the hiding domain classification problem. The proposed scheme has demonstrated good classification results

Detecting frame duplication based on spatial and temporal analyses

A passive scheme is developed to achieve frame duplication detection for videos in the paper. The proposed scheme is a coarse-to-fine approach and composed of candidate clip selection, spatial correlation calculation, and frame duplication classification. To screen duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in RGB color space is adopted. To evaluate the similarity of image content, a block-based algorithm is used to measure spatial correlation of each corresponding frame between the query clip and the candidate one. Based on the spatial and temporal analyses in the temporal and spatial domains, we can localize the duplicated clips. The experimental results show that our scheme can not only detect but also localize duplicated clips well. In addition, the proposed scheme outperforms an existing method in terms of false alarm, missed detection, and accuracy.