Yongjian Hu

DE-Based Reversible Data Hiding With Improved Overflow Location Map

For difference-expansion (DE)-based reversible data hiding, the embedded bit-stream mainly consists of two parts: one part that conveys the secret message and the other part that contains embedding information, including the 2-D binary (overflow) location map and the header file. The first part is the payload while the second part is the auxiliary information package for blind detection. To increase embedding capacity, we have to make the size of the second part as small as possible. Tians classical DE method has a large auxiliary information package. Thodi mitigated the problem by using a payload-independent overflow location map. However, the compressibility of the overflow location map is still undesirable in some image types. In this paper, we focus on improving the overflow location map. We design a new embedding scheme that helps us construct an efficient payload-dependent overflow location map. Such an overflow location map has good compressibility. Our accurate capacity control capability also reduces unnecessary alteration to the image. Under the same image quality, the proposed algorithm often has larger embedding capacity. It performs well in different types of images, including those where other algorithms often have difficulty in acquiring good embedding capacity and high image quality.

Reversible Visible Watermarking and Lossless Recovery of Original Images

In this paper, we propose a reversible visible watermarking algorithm to satisfy a new application scenario where the visible watermark serves as a tag or ownership identifier, but can be completely removed to resume the original image data. It includes two procedures: data hiding and visible watermark embedding. In order to losslessly recover both the watermark-covered and nonwatermark-covered image contents at the receiver end, the payload consists of two reconstruction data packets, one for recovering the watermark-covered region, and the other for the nonwatermark-covered region. The data hiding technique reversibly hides the payload in the image region not covered by the visible watermark. To satisfy the requirements of large capacity and high image quality, our hiding technique is based on data compression and uses a payload-adaptive scheme. It further adopts error diffusion for improving subjective image quality and arithmetic compression using a character-based model for increasing computational efficiency. The visible watermark is securely embedded based on a user-key-controlled embedding mechanism. The data hiding and the visible watermark embedding procedures are integrated into a secure watermarking system by a specially designed user key

Difference Expansion Based Reversible Data Hiding Using Two Embedding Directions

Current difference-expansion (DE) embedding techniques perform one layer embedding in a difference image. They do not turn to the next difference image for another layer embedding unless the current difference image has no expandable differences left. The obvious disadvantage of these techniques is that image quality may have been severely degraded even before the later layer embedding begins because the previous layer embedding has used up all expandable differences, including those with large magnitude. Based on integer Haar wavelet transform, we propose a new DE embedding algorithm, which utilizes the horizontal as well as vertical difference images for data hiding. We introduce a dynamical expandable difference search and selection mechanism. This mechanism gives even chances to small differences in two difference images and effectively avoids the situation that the largest differences in the first difference image are used up while there is almost no chance to embed in small differences of the second difference image. We also present an improved histogram-based difference selection and shifting scheme, which refines our algorithm and makes it resilient to different types of images. Compared with current algorithms, the proposed algorithm often has better embedding capacity versus image quality performance. The advantage of our algorithm is more obvious near the embedding rate of 0.5 bpp.

A high capacity lossless data hiding scheme for JPEG images

In this paper, we propose a new high-capacity reversible data hiding method for JPEG-compressed images. This method is based on modifying the quantization table and quantized discrete cosine transformation (DCT) coefficients. Some elements of the quantization table are divided by an integer while the corresponding quantized DCT coefficients are multiplied by the same integer and added by an adjustment value to make space for embedding the data. By analyzing the effect of each single quantized DCT coefficient on the image quality, an embedding sequence is chosen in order to help control the increase of file size after hiding the data meanwhile the PSNR value between the original uncompressed image and stego JPEG image is high. Experimental results show that the proposed method achieves both high capacity and high image quality.

Source Camera Identification Using Large Components of Sensor Pattern Noise

Digital image forensics has attracted a lot of attention recently for its role in identifying the origin of digital image. Although different forensic approaches have been proposed, one of the most popular approaches is to rely on the imaging sensor pattern noise, where each sensor pattern noise uniquely corresponds to an imaging device and serves as the intrinsic fingerprint. The correlation-based detection is heavily dependent upon the accuracy of the extracted pattern noise. In this work, we discuss the way to extract the pattern noise, in particular, explore the way to make better use of the pattern noise. Unlike current methods that directly compare the whole pattern noise signal with the reference one, we propose to only compare the large components of these two signals. Our detector can better identify the images taken by different cameras. In the meantime, it needs less computational complexity.

Robust Clothing-Invariant Gait Recognition

Robust gait recognition is a challenging problem, due to the large intra-subject variations and small inter-subject variations. Out of the covariate factors like shoe type, carrying condition, elapsed time, it has been demonstrated that clothing is the most challenging covariate factor for appearance-based gait recognition. For example, long coat may cover a significant amount of gait features and make it difficult for individual recognition. In this paper, we proposed a random subspace method (RSM) framework for clothing-invariant gait recognition by combining multiple inductive biases for classification. Even for small size training set, this method can achieve promising performance. Experiments are conducted on the OU-ISIR Treadmill dataset B which includes 32 combinations of clothing types, and the average recognition accuracy is more than 80%, which indicates the effectiveness of our proposed method.

On classification of source cameras: A graph based approach

Many existing source camera classification methods involve either training a classifier or computing the reference pattern noise of a camera, which means a set of images of known origins have to be pre-acquired. However, such requirement can not always be satisfied in real-world forensic applications. In this work, we propose a graph based approach that requires no extra auxiliary images nor a prior knowledge about the constitution of the image set. By formulating the classification task as a graph partitioning problem, a set of images can be classified according to their source cameras in an entirely blind way, with the number of source cameras automatically estimated. Experimental results have verified the validity of the proposed approach.

Using invisible watermarks to protect visibly watermarked images

Physical visible watermarks have been widely used for centuries. Now digital visible watermarks such as electronic logos find their applications in digital library, video broadcasting, and other multimedia services. Several visible watermarking techniques have been proposed in the literature, and meanwhile, some problems with visible watermarks are also under investigation. Among these problems, watermark removal and unauthorized insertion are two major concerns. We propose using an invisible watermark in visibly watermarked images to overcome these problems. When a visibly watermarked image is in question, the invisible watermark can provide appropriate ownership information. We first investigate what kind of invisible watermark is needed, and then, focus on the details of the invisible watermarking technique. The experiments have shown that the proposed algorithm can provide a very effective protection for visibly watermarked images.

Source camera identification from significant noise residual regions

This paper investigates the digital forensic problem of determining whether an image has been produced by a specific digital camera. We employ the binary hypothesis testing scheme to detect the presence of photo-response non-uniformity( PRNU) in the image. The main challenge of this scheme is the extremely weak amount of PRNU in the observed noise residual. We propose to extract from the noise residual the significant regions with higher signal quality and discard those regions heavily deteriorated by irrelevant noises. Experimental results demonstrate that the proposed algorithm can improve the identification performance in the sense of decreasing the false rejection rate, which is a critical measure in practical applications.

Random Subspace Method for Gait Recognition

Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.