Hierarchical watermarking for secure image authentication with localization

A block-based watermarking scheme for image tamper detection and self-recovery

Several fragile watermarking techniques cannot detect vector quantization (VQ) attacks; the block-based approach, for example. Celik et al. (IEEE Trans. Image Processing, vol. 11, no. 6, pp. 585-595, 2002) proposed a hierarchical watermarking approach to reveal the VQ attacks. In order to improve the hierarchical watermarking approach, we present a new fragile watermarking technique based on genetic algorithms (GAs) to embed watermarks into the frequency domain of a host image. Different from existing schemes, the adjustment in the host image by GAs will achieve the embedding of fragile watermarks. The technique can provide a fundamental platform for other fragile watermarking techniques. Furthermore, the embedding of watermarks into frequency domain enhances the security in fragile watermarking

The Laplacian pyramid is a versatile data structure that represents an image as a sequence of spatially filtered and decimated versions of the original image. To obtain an expanded approximation from the processed decimated samples, linear interpolation is commonly used due to its computational simplicity; however, linear interpolation does not consider the gray-level change in the neighborhood of each interpolated pixel, and thus creates unpleasant staircase effects near edges and visually annoying blocking effects in shade regions. This paper proposes a new pyramidal image coding algorithm using contour-based interpolative vector quantization (VQ) to obtain a perceptually better interpolated image through the use of edge information at each level of the pyramid. The basic idea is to code the image using VQ on a level-by-level basis in the hierarchical structure. The decimation implies that the redundancy existing over large image areas can be efficiently exploited by VQ of a low dimension. In order to improve the quality of the reconstructed image, an adaptive corrector stage is added at the final stage of the pyramid to selectively encode the residual errors as needed. The locations for correction are hierarchically predicted using the previously encoded data at the higher levels. The above scheme is a very efficient combination of VQ and contour-based interpolation in the hierarchical pyramid structure, and is amenable to progressive image transmission. Experimental results show that good reconstructed images are obtained at 0.25 bits per pixel with a reasonable coding complexity.

In this paper, we propose an efficient watermarking algorithm for image tamper localization and recovery. To enhance the accuracy of tamper localization, the algorithm contains basic authentication and refining authentication. Basic authentication can locate all blocks that contain the tampered pixels by the basic authentication watermark. Refining authentication can exclude some genuine pixels from the tampered block by the refining authentication watermark that is generated based on image disordering. Finally, we fuse the two authentication results and take some post-processing measures to obtain the accuracy authentication result. The recovery watermark is the feature of every block, which is generated using the important DCT coefficients. Theoretical analysis and simulation results show that the proposed algorithm can improve the accuracy of tamper localization. At the same time, the algorithm can also approximately recover the tampered pixels.

In previous work, Wong had proposed both secret key and public key watermarking schemes for image authentication that can detect and localized any change made to a watermarked image. The techniques proposed were block-based, that is, they partitioned the image into non-overlapping blocks and separately authenticated each block. Subsequently, Holliman and Memon observed that many block based watermarking schemes are vulnerable to substitution attacks. They specifically showed that the Wong schemes can be attacked using a 'vector quantization' (VQ) approach. This attack exploits that fact that if a sufficient number of images containing the same watermark bitmap is available, then one can use a VQ-like technique to forge a watermark into a new image. About the same time and independently, Coppersmith et al. proposed to use overlapping blocks to resist this attack. Although this method can make the attack inefficient, it does so with a significant loss of the localization property of the watermark. We extend in this paper the Wong schemes so that the resulting algorithms can resist the VQ attack and at the same time provide the same localization property in the watermark as the original schemes. The key idea is to insert a unique image-dependent block ID into the watermarking process so that the VQ attack will not have a rich enough 'codebook' to forge the watermark.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents an efficient authentication method for JPEG images based on Genetic Algorithms (GA). The current authentication methods for JPEG images require the receivers to know the quantization table beforehand in order to authenticate the images. Moreover, the quantization tables used in the JPEG compression are different for different quality factors, thus increasing the burden on the receivers to maintain several quantization tables. We propose a novel GA-based method which possesses three advantages. First, the computation at the receiver end is simplified. Second, it is no more required for the receivers to maintain quantization tables. Third, the method is resistant against Vector Quantization (VQ) and Copy-Paste (CP) attacks by generating the authentication information which is unique with respect to each block and each image. Furthermore, we develop a two- level detection strategy to reduce the false acceptance ratio of invalid blocks. Experimental results show that the proposed GA-based method can successfully authenticate JPEG images under variant attacks.

For image content authentication, a secure watermarking method using quantization-based embedding on the largest singular value (SV) is proposed. The block-wise quantization-based embedding can be vulnerable to vector quantization (VQ) attack and attacks associated with histogram analysis. To overcome these security problems, the proposed method places interdependency among image blocks and dithers the quantized value. By adjusting the threshold of the detector, a trade-off between the robustness to JPEG compression and the probability of misdetection can be made. The proposed method can detect a tampered area with high sensitivity. This is confirmed by experimental results and security analysis.

This paper presents a study on the benefits of multi-level hierarchy for scalable mobility management in the wireless mobile IP context. Micro mobility management is considered. The analysis took advantage of the locality in movement and call activities of mobile users. It was discovered that whether multi-level hierarchy is beneficial or not depends on the type of address management employed in the mobility management. When hierarchical address structure is used in a micro mobility region, the system scales with more levels of hierarchy, but if the address structure used is not hierarchical, multi-level hierarchy is less scalable because the root node presents a bottleneck to the system.

More than ever, the practical and accurate watermarking technologies are called for the growing amount of exchanged digital image over the Internet. To protect the integrity and authenticity of digital image and to enhance the effect of tamper detection and restoration, we design and implement a semi-fragile watermark based on cat transformation, mostly used to locate tamper and recover for the transformed image and plain-image. The watermark which consists of two parts: the authentication watermark and recovery watermark, is embedded into the 2 least significant bit (LSB) of the pixel of the original image. The authentication watermark is calculated by the pixel value comparison and the parity check code, while the recovery watermark contains the average pixel value of the Torus image block. In the detection side, we use the hierarchy concept to locate the tamper in three layers and recover the attacked image in two layers. By using the hierarchy concept, this algorithm has another superiority that tamper can be detected on confused image. The experimental results show that our algorithm can accurately locate tamper and realize the content recovery and effectively prevent the vector quantization attack. Compared with other algorithms, this algorithm has better effect of tamper location and recovery.

Pinpoint authentication watermarking based on a chaotic system

In the pyramid transform coding, the input image of size 2n × 2n is first decomposed into a set of blocks of size 2m × 2m, where m<n. Then, each block undergoes a two-dimensional discrete cosine transform 2D-DCT of size 2m × 2m. The resulting coefficients are reorganised into 2m × 2m transform subimages of size 2n-m × 2n-m each corresponding to a coefficient order. For each subimage, a transform mean subpyramid is formed by successively averaging over 2 × 2 neighbouring values. The corresponding transform difference subpyramid is then built up by taking the differences between successive levels in the transform mean subpyramid. The 2m × 2m transform difference subpyramids are concatenated and rearranged to form a transform difference pyramid. The transform difference pyramid can be used to form a transform mean pyramid, where the intermediate levels of the transform mean pyramid are simply the reduced-size approximations of the block transformed image. This implies that progressive image transmission can be achieved by transmitting the transform difference pyramid. The transform difference pyramid is shown to reduce the correlation between the neighbouring transform blocks. Therefore, more efficient transmission is obtained by transmitting the transform difference pyramid, instead of the original block DCT image and the transform mean pyramid. Before transmission, the transform difference pyramid is either scale or vector quantised and encoded starting from the top level where the residual errors introduced at each level are delivered to and reprocessed at the next level. The simulation results demonstrate that, by using vector quantisation, excellent quality is achieved at a bit rate of about 0.55 bits/ pixel. The pyramid transform coding scheme is shown to be competitive with variable-length transform coding.

Statistical sensitivity analysis (SSA) is an effective methodology to examine the impact of variations in model inputs on the variations in model outputs at either a prior or posterior design stage. A hierarchical statistical sensitivity analysis (HSSA) method has been proposed in literature to incorporate SSA in designing complex engineering systems with a hierarchical structure. However, the original HSSA method only deals with hierarchical systems with independent subsystems. Due to the existence of shared variables at lower levels, responses from lower level submodels that act as inputs to a higher level subsystem are both functionally and statistically dependent. For designing engineering systems with dependent subsystem responses, an extended hierarchical statistical sensitivity analysis (EHSSA) method is developed in this work to provide a ranking order based on the impact of lower level model inputs on the top level system performance. A top-down strategy, same as in the original HSSA method, is employed to direct SSA from the top level to lower levels. To overcome the limitation of the original HSSA method, the concept of a subset SSA is utilized to group a set of dependent responses from lower level submodels in the upper level SSA. For variance decomposition at a lower level, the covariance of dependent responses is decomposed into the contributions from individual shared variables. To estimate the global impact of lower level inputs on the top level output, an extended aggregation formulation is developed to integrate local submodel SSA results. The importance sampling technique is also introduced to re-use the existing data from submodels SSA during the aggregation process. The effectiveness of the proposed EHSSA method is illustrated via a mathematical example and a multiscale design problem.

Plant data are used to compare the effectiveness of wavelet-based methods with other compression techniques. The challenge is to effectively treat the data so that the maximum compression ratio is achieved while the important features are retained in the compressed data. Wavelets have properties that are desirable for data compression. They are localized in time (or space) and in frequency. This means that important short-lived high-frequency disturbances can be preserved in the compressed data, and these disturbances may be differentiated from slower, low-frequency trends. Besides discrete wavelet transforms, linear interpolation, discrete cosine transform, and vector quantization are also used to compress data. The transform-based compression algorithms perform better than the linear interpolation methods, such as swinging door, that have been used traditionally in the chemical process industries. Among these techniques, the wavelet-based one compresses the process data with excellent overall and best local accuracy.

Image compression operator based on discrete cosine transform was brought up. A securer scrambling locational operator was put forward based on the concept of anti-tamper radius. The basic idea of the algorithm is that it first combined image block compressed data with eigenvalue of image block and its offset block, then scrambled or encrypted and embeded them into least significant bit of corresponding offset block. This algorithm could pinpoint tampered image block and tampering type accurately. It could recover tampered block with good image quality when tamper occured within the limits of the anti-tamper radius. It could effectively resist vector quantization and synchronous counterfeiting attacks on self-embedding watermarking schemes.

Image compression has experienced a new revolution with the success of deep learning, which yields superior rate-distortion (RD) performance against traditional codecs. Yet, high computational complexity and energy consumption are the main bottlenecks that hinder the practical applicability of deep-learning-based (DL-based) codecs. Inspired by the neural network's hierarchic structure yet with lower complexity, we propose a new lightweight image coding framework and name it the "Green Image Codec" (GIC) in this work. First, GIC down-samples an input image into several spatial resolutions from fine-to-coarse grids and computes image residuals between two adjacent grids. Then, it encodes the coarsest content, interpolates content from coarse-tofine grids, encodes residuals, and adds residuals to interpolated images for reconstruction. All coding steps are implemented by vector quantization while all interpolation steps are conducted by the Lanczos interpolation. To facilitate VQ codebook training, the Saab transform is applied for energy compaction and, thus, dimension reduction. A simple rate-distortion optimization (RDO) is developed to help select the coding parameters. GIC yields an RD performance that is comparable with BPG at significantly lower complexity.