Chia-Chen Lin

Multilevel reversible data hiding based on histogram modification of difference images

Reversible data hiding has drawn considerable attention in recent years. Reversibility allows original media to be completely recovered from marked media without distortion after embedded message has been extracted. In this paper we propose a multilevel reversible data hiding scheme based on the difference image histogram modification that uses the peak point to hide messages. Through a joint imperceptibility and hiding capacity evaluation, we show that our proposed scheme uses a multilevel hiding strategy to achieve large hiding capacity and keep distortion low. Performance comparisons with other existing reversible hiding schemes are provided to demonstrate the validity of our proposed scheme.

Reversible hiding in DCT-based compressed images

This paper presents a lossless and reversible steganography scheme for hiding secret data in each block of quantized discrete cosine transformation (DCT) coefficients in JPEG images. In this scheme, the two successive zero coefficients of the medium-frequency components in each block are used to hide the secret data. Furthermore, the scheme modifies the quantization table to maintain the quality of the stego-image. Experimental results also confirm that the proposed scheme can provide expected acceptable image quality of stego-images and successfully achieve reversibility.

A Reversible Data Hiding Scheme Based on Side Match Vector Quantization

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme

A novel secret image sharing scheme in color images using small shadow images

Over the past several years, secret image sharing techniques have become another branch of the effort to prevent secret images from being eavesdropped on, in addition to traditional cryptography. Because smaller shadows can speed up the transmission of a secret color image, in this paper we combine Chang and Wus gradual search algorithm for a single bitmap BTC (GSBTC) and Shamirs (k,n) threshold concept to propose a novel secret color image sharing scheme that generates smaller shadows. Experimental results confirm that the proposed scheme successfully reduces shadow size and that each shadow behaves as a random-like image that prevents leakage of information about the secret color image. Furthermore, the correlation between two vertically or horizontally adjacent pixels in each shadow is significantly less than those in a color secret image, and the presented scheme also achieves, on average, an NPCR=0.414% and AUCI=32.78%. Thus, with our scheme one-pixel difference could cause a significant difference in the corresponding shadows. Therefore, the security of the presented scheme is also confirmed.

Brain Tumor Detection Using Color-Based K-Means Clustering Segmentation

In this paper, we propose a color-based segmentation method that uses the K-means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color-based segmentation algorithm with K-means is to convert a given gray-level MR image into a color space image and then separate the position of tumor objects from other items of an MR image by using K-means clustering and histogram-clustering. Experiments demonstrate that the method can successfully achieve segmentation for MR brain images to help pathologists distinguish exactly lesion size and region.

A reversible data hiding scheme for VQ indices using locally adaptive coding

Data hiding is designed to solve the problem of secure information exchange through public networks such as Internet. In this paper, we present an improved reversible data hiding scheme that can recover original VQ indices after data extraction. As with Chang et al.s scheme, our proposed scheme also depends on the locally adaptive coding scheme. However, experimental results confirm that the hiding capacity of our proposed scheme is around 1.36 bpi in most digital images, which is typically higher than that of Chang et al.s [17]. Moreover, the average compression rate that can be achieved with our proposed scheme is 0.49 bpp, which outperforms both Lin and Changs scheme (0.50bpp), Tsai (0.50 bpp), Chang et al.s scheme (0.53 bpp), and Yang and Lins scheme (0.53 bpp).

A novel VQ-based reversible data hiding scheme by using hybrid encoding strategies

Highlights? The secret data can be embedded into the transformed index table of a cover image. ? The average compression rate of our scheme is 0.325bpp and it is much better than Chen and Huangs and Chang et al.s schemes. ? Our embedding and extracting times are 5.491s and 0.352s. Reversible data hiding is a special algorithm that not only guarantees the confidential data will be extracted accurately but also allows the original cover image to be reconstructed without distortion after the confidential data are completely extracted. This paper proposes a new index compression and reversible data hiding scheme based on side-match vector quantization (SMVQ) and search order coding (SOC). In this proposed scheme, the confidential data are embedded into the transformed index table of a cover image. During the extracting phase, simple steps are employed to extract the confidential data and reconstruct the original cover image. The experimental results show that with a codebook size of 256, the average compression rate of the proposed scheme is 0.325bpp, which is superior to that of the methods proposed by Chen and Huang (0.426bpp) and Chang et al. (0.429bpp). Additionally, the embedding and extracting times of our scheme are 5.491s and 0.352s, respectively, demonstrating that the execution time of the proposed scheme is much faster than that of the methods of Chen and Huang and Chang et al. Moreover, our scheme achieves better performance than other selected reversible data hiding schemes with respect to the data embedding and data compression rates.

Novel image copy detection with rotating tolerance

In 2003, Kim applied the discrete cosine transform (DCT) technique to propose a content-based image copy detection method. He successfully detected the copies both with and without modifications, and his method is the first to detect the copies with water coloring and twirling modifications. However, Kims method can only detect copies having a 180^o rotation. When copies have a 90^o or 270^o rotation, Kims method fails to discover them. Also, his method cannot deal with copies having only minor rotations of 1^o or 5^o, and so on. To conquer this weakness, we propose an elliptical track division strategy to extract two kinds of features in the design of our proposed methods. Furthermore, we propose a dynamic center point detection mechanism for the elliptical track of each image to deal with the shifting process. The experimental results confirm that both proposed methods can successfully capture the features of an image even if it is shifted, cropped or rotated to any degree. In addition, our hybrid method can further provide accurate detection performance for a variety of manipulations.

A data embedding scheme for color images based on genetic algorithm and absolute moment block truncation coding

Recently, embedding a large amount of secret data into gray-level and color images with low distortion has become an important research issue in steganography. In this paper, we propose a data embedding scheme by using a well-known genetic algorithm, block truncation code and modification direction techniques to embed secret data into compression codes of color images to expand the variety of cover media. In the scheme, the common bitmap generation procedure of GA-AMBTC has been modified to speed up the hiding procedure. Two embedding strategies are proposed to hide secret data into the common bitmap and the quantization values in each block of the cover image. Experimental results confirm that the proposed scheme can provide high data capacity with acceptable image quality of the stego-images. Moreover, the compression ratio of the scheme is exactly the same as that of GA-AMBTC so that attackers cannot detect any trace of hidden data from the size of the modified compressed result.

Sharing a verifiable secret image using two shadows

In this paper, we propose a novel (2,2) verifiable secret sharing (VSS) scheme, which not only protects a secret image but also allows users to verify the restored secret image in the revealing and verifying phase, for all binary, grayscale and color images. Error diffusion and image clustering techniques are adopted to achieve our objective. Experimental results and discussions show that the proposed scheme, with its smaller shadow size and lower computational complexity, obviously outperforms previous VSS schemes designed either with or without the cheating prevention mechanism. Moreover, the use of a halftone logo gives an efficient solution to verifying whether the restored secret image is correct by using a halftone logo.