A Inverse Halftoning Technique Using Modified Look-Up Tables

[[abstract]]In this paper, we shall propose a method to invert halftone images. We shall use a modified look-up table to reconstruct gray level images from halftone images. In the modified look-up table training phase, we shall use a sliding window to get halftone patterns and their corresponding gray-level lists. The modified look-up table can be obtained by calculating the centroid of the gray-level lists. In the image reconstruction phase, we shall use the sliding window to slide around the pixel to reconstruct so that we can get candidate gray values. The reconstructed gray value can be obtained by calculating the centroid of the candidate gray values. Experimental results have shown that our method indeed can get satisfied results

Image Hiding Scheme with Modulus Function and Dynamic Programming Strategy on Partitioned Pixels",

[[abstract]]In this paper, we shall propose a novel image-hiding scheme. Our new scheme classifies the host image pixels into two groups of pixels according to the pixel values. For each group of pixels, the corresponding secret pixel values go through an optimal substitution process and are transformed into other pixel values by following the dynamic programming strategy. Then, we can embed the transformed pixel values in the host pixels by using the modulus functions and obtain the stego-image. Extensive experimental results demonstrate that our new method is capable of offering better stego-image quality than a number of well-accepted schemes

An Efficient Image Authentication Method Based on Hamming Code

[[abstract]]Image authentication has come through a history of several years. However, up to the present time, most mainstream image authentication schemes are still unable to detect burst bit errors. Moreover, the capability of recovering tampered pixels in detail (complex) areas has not been very satisfactory either. In this paper, we offer to combine the Hamming code technique, Torus automorphism and bit rotation technique to do tamper proofing. According to our experimental results, our new hybrid method can effectively eliminate burst bit errors, and our recovered pixels in detail areas can actually gain very high clarity. The results show that our scheme is quite a practical method, which is quite able to detect and recover tampered areas

An Image Hiding Scheme Based on Multi-bit-reference Substitution Table Using Dynamic Programming Strategy

[[abstract]]Simple least-significant-bit (LSB) substitution is the most straightforward way to embed the secret image in the host image. Based on the simple LSB substitution, the method using substitution table is proposed to improve the quality of the stego-image. In this paper, we shall bring up a new method that uses the un-embedded host pixel bits to partition the host pixel into different planes. This way, we can derive the optimal substitution table for each plane. By combining the optimal substitution tables, we can obtain the final result that we call the multi-bit-reference substitution table. After transforming the secret data according multi-bit-reference substitution table, we can embed the transformed secret data in the host image so that the host image will be degraded possibly less. The experimental results show that our method leads to good results

On Using LSB Matching Function for Data Hiding in Pixels

[[abstract]]Abstract: The proposed method in this paper is based on the LSB matching function of Mielikainen for Steganography. However, there is a significant change with respect to Mielikainen's method in the use of the LSB matching function to compute secret bits. Melikainen extracts secret bits in pixel pairs: the first one is the LSB of the first pixel in a pixel pair and the other is computed by a LSB matching function applied to both pixels. The proposed method does not make any partition, but makes the sequential image processing: the current secret bit is always computed by a LSB matching function where the LSB matching function is modified using only an XOR operation. To make the data extraction compatible with data embedding, the proposed method modifies sparse pixels by adding/subtracting their value to/from one. In the experimental results, the values of embedding efficiency increase while the involved bits increase. Taking test images in the experiments, the numbers of modified pixels in the proposed method are always lower than those in Mielikainen's method

Placing Chinese Phonetic Symbols on Mobile Phones Keyboard Using Branch-and-bound Strategy

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An information hiding scheme by applying the dynamic programming strategy to LSB matching revisited

[[abstract]]In 2006, Mielikainen proposed LSB matching revisited to achieve image hiding. This method is able to embed two secret bits into a pair of cover pixels by adding or subtracting one from either of the pixel values of the cover pixels. In this paper we apply a dynamic programming strategy to LSB matching revisited, so as to further reduce the number of modified pixels. First, our method produces an optimal substitution table for LSB matching revisited by using the dynamic programming strategy. The secret data are then transformed according to the substitution table. Finally, the transformed secret data are embedded into a cover image by using LSB matching revisited. As shown in our experimental results, the number of modified pixels in our method is fewer than that in LSB matching revisited

On using LSB matching function for data hiding in pixels

[[abstract]]The proposedmethod in this paper is based on the LSB matching function ofMielikainen for Steganography. However, there is a significant change with respect to Mielikainen's method in the use of the LSB matching function to compute secret bits. Melikainen extracts secret bits in pixel pairs: the first one is the LSB of the first pixel in a pixel pair and the other is computed by a LSB matching function applied to both pixels. The proposed method does not make any partition, but makes the sequential image processing: the current secret bit is always computed by a LSB matching function where the LSB matching function is modified using only an XOR operation. To make the data extraction compatible with data embedding, the proposed method modifies sparse pixels by adding/subtracting their value to/from one. In the experimental results, the values of embedding efficiency increase while the involved bits increase. Taking test images in the experiments, the numbers of modified pixels in the proposed method are always lower than those in Mielikainen's method

A Survey of Information Hiding Schemes for Digital Images

[[abstract]]In this survey, we first describe the purposes for image hiding. They are Steganography, Image Watermarking and Image Authentication. Classified by hiding methods, image hiding can be divided into pixel-based image hiding, frequency-based image hiding and VQ-based image hiding. In the aspect of pixel-based image hiding, we describe three representative methods, the substitution table technique, the modulus function technique, and the LSB matching revisited technique. As for frequency-based and VQ-based image hiding, we introduce methods to do Steganography, Image Watermarking and Image Authentication by applying image compression methods, JPEG and Vector Quantization