Yun Q. Shi

Reversible Watermarking Algorithm Using Sorting and Prediction

This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

A Markov process based approach to effective attacking JPEG steganography

In this paper, a novel steganalysis scheme is presented to effectively detect the advanced JPEG steganography. For this purpose, we first choose to work on JPEG 2-D arrays formed from the magnitudes of quantized block DCT coefficients. Difference JPEG 2-D arrays along horizontal, vertical, and diagonal directions are then used to enhance changes caused by JPEG steganography. Markov process is applied to modeling these difference JPEG 2-D arrays so as to utilize the second order statistics for steganalysis. In addition to the utilization of difference JPEG 2-D arrays, a thresholding technique is developed to greatly reduce the dimensionality of transition probability matrices, i.e., the dimensionality of feature vectors, thus making the computational complexity of the proposed scheme manageable. The experimental works are presented to demonstrate that the proposed scheme has outperformed the existing steganalyzers in attacking OutGuess, F5, and MB1.

A DWT-DFT composite watermarking scheme robust to both affine transform and JPEG compression

Robustness is a crucially important issue in watermarking. Robustness against geometric distortion and JPEG compression at the same time with blind extraction remains especially challenging. A blind discrete wavelet transform-discrete Fourier transform (DWT-DFT) composite image watermarking algorithm that is robust against both affine transformation and JPEG compression is proposed. The algorithm improves robustness by using a new embedding strategy, watermark structure, 2D interleaving, and synchronization technique. A spread-spectrum-based informative watermark with a training sequence is embedded in the coefficients of the LL subband in the DWT domain while a template is embedded in the middle frequency components in the DFT domain. In watermark extraction, we first detect the template in a possibly corrupted watermarked image to obtain the parameters of an affine transform and convert the image back to its original shape. Then, we perform translation registration using the training sequence embedded in the DWT domain, and, finally, extract the informative watermark. Experimental work demonstrates that the proposed algorithm generates a more robust watermark than other reported watermarking algorithms. Specifically it is robust simultaneously against almost all affine transform related testing functions in StirMark 3.1 and JPEG compression with quality factor as low as 10. While the approach is presented for gray-level images, it can also be applied to color images and video sequences.

A Novel Difference Expansion Transform for Reversible Data Embedding

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

Efficiently self-synchronized audio watermarking for assured audio data transmission

In this paper, we propose a self-synchronization algorithm for audio watermarking to facilitate assured audio data transmission. The synchronization codes are embedded into audio with the informative data, thus the embedded data have the self-synchronization ability. To achieve robustness, we embed the synchronization codes and the hidden informative data into the low frequency coefficients in DWT (discrete wavelet transform) domain. By exploiting the time-frequency localization characteristics of DWT, the computational load in searching synchronization codes has been dramatically reduced, thus resolving the contending requirements between robustness of hidden data and efficiency of synchronization codes searching. The performance of the proposed scheme in terms of SNR (signal to noise ratio) and BER (bit error rate) is analyzed. An estimation formula that connects SNR with embedding strength has been provided to ensure the transparency of embedded data. BER under Gaussian noise corruption has been estimated to evaluate the performance of the proposed scheme. The experimental results are presented to demonstrate that the embedded data are robust against most common signal processing and attacks, such as Gaussian noise corruption, resampling, requantization, cropping, and MP3 compression.

A generalized Benford's law for JPEG coefficients and its applications in image forensics

In this paper, a novel statistical model based on Benfords law for the probability distributions of the first digits of the block-DCT and quantized JPEG coefficients is presented. A parametric logarithmic law, i.e., the generalized Benfords law, is formulated. Furthermore, some potential applications of this model in image forensics are discussed in this paper, which include the detection of JPEG compression for images in bitmap format, the estimation of JPEG compression Qfactor for JPEG compressed bitmap image, and the detection of double compressed JPEG image. The results of our extensive experiments demonstrate the effectiveness of the proposed statistical model.

Robust Lossless Image Data Hiding Designed for Semi-Fragile Image Authentication

Recently, among various data hiding techniques, a new subset, lossless data hiding, has received increasing interest. Most of the existing lossless data hiding algorithms are, however, fragile in the sense that the hidden data cannot be extracted out correctly after compression or other incidental alteration has been applied to the stego-image. The only existing semi-fragile (referred to as robust in this paper) lossless data hiding technique, which is robust against high-quality JPEG compression, is based on modulo-256 addition to achieve losslessness. In this paper, we first point out that this technique has suffered from the annoying salt-and-pepper noise caused by using modulo-256 addition to prevent overflow/underflow. We then propose a novel robust lossless data hiding technique, which does not generate salt-and-pepper noise. By identifying a robust statistical quantity based on the patchwork theory and employing it to embed data, differentiating the bit-embedding process based on the pixel groups distribution characteristics, and using error correction codes and permutation scheme, this technique has achieved both losslessness and robustness. It has been successfully applied to many images, thus demonstrating its generality. The experimental results show that the high visual quality of stego-images, the data embedding capacity, and the robustness of the proposed lossless data hiding scheme against compression are acceptable for many applications, including semi-fragile image authentication. Specifically, it has been successfully applied to authenticate losslessly compressed JPEG2000 images, followed by possible transcoding. It is expected that this new robust lossless data hiding algorithm can be readily applied in the medical field, law enforcement, remote sensing and other areas, where the recovery of original images is desired.

Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions

In this paper, a steganalysis scheme based on multiple features formed by statistical moments of wavelet characteristic functions is proposed. Our theoretical analysis has pointed out that the defined n-th statistical moment of a wavelet characteristic function is related to the n-th derivative of the corresponding wavelet histogram, and hence is sensitive to data embedding. The selection of the first three moments of the characteristic functions of wavelet subbands of the three-level Haar wavelet decomposition as well as the test image has resulted in total 39 features for steganalysis. The effectiveness of the proposed system has been demonstrated by extensive experimental investigation. The detection rate for Cox et al.s non-blind spread spectrum (SS) data hiding method, Piva et al.s blind SS method, Huang and Shis 8×8 block SS method, a generic LSB method (as embedding capacity being 0.3 bpp), and a generic QIM method (as embedding capacity being 0.1 bpp) are all above 90% over all of the 1096 images in the CorelDraw image database using the Bayes classifier. Furthermore, when these five typical data hiding methods are jointly considered for steganalysis, i.e., when the proposed steganalysis scheme is first trained sequentially for each of these five methods, and is then tested blindly for stego-images generated by all of these methods, the success classification rate is 86%, thus pointing out a new promising approach to general blind steganalysis. The detection results of steganalysis on Jsteg, Outguess and F5 have further demonstrated the effectiveness of the proposed steganalysis scheme.

A natural image model approach to splicing detection

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this paper, we propose a blind, passive, yet effective splicing detection approach based on a natural image model. This natural image model consists of statistical features extracted from the given test image as well as 2-D arrays generated by applying to the test images multi-size block discrete cosine transform (MBDCT). The statistical features include moments of characteristic functions of wavelet subbands and Markov transition probabilities of difference 2-D arrays. To evaluate the performance of our proposed model, we further present a concrete implementation of this model that has been designed for and applied to the Columbia Image Splicing Detection Evaluation Dataset. Our experimental works have demonstrated that this new splicing detection scheme outperforms the state of the art by a significant margin when applied to the above-mentioned dataset, indicating that the proposed approach possesses promising capability in splicing detection.

Image steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network

In this paper, a general blind image steganalysis system is proposed, in which the statistical moments of characteristic functions of the prediction-error image, the test image, and their wavelet subbands are selected as features. Artificial neural network is utilized as the classifier. The performance of the proposed steganalysis system is significantly superior to the prior arts.