Image width is an important factor for making the partially recovered data perceptually meaningful in image file carving. The authors conduct a comprehensive comparison of the performance of the representative methods for estimating the JPEG image width. Experimental results show that the best methods based on pixels are always better than the best methods based on quantised discrete cosine transform (DCT) coefficients. To keep the good performance of the pixel‐based methods when the correct quantisation tables are unavailable, the authors replace the correct quantisation tables with the standard ones. Experimental results certify that such a replacement has only a little effect on the performance of the pixel‐based methods, the best of which still outperform the best methods based on quantised DCT coefficients. The two results indicate that it may be enough to just focus on the pixel‐based methods for future work. Finally, they propose a pixel‐based method, which derives the candidate image widths from the most likely adjacent minimum coded unit (MCU) pairs in the vertical direction. The candidate width which appears most frequently is chosen as the estimated image width. Experimental results show that the proposed method usually has the best performance when most MCUs of an image are recovered.

Successive cancellation (SC) is the first and widely known decoder of polar codes, which has received a lot of attentions. However, its decoding schedule generating methods are still primitive. Based on a newly found factor, this study proposes an online method to generate the decoding schedule of SC decoder by a non‐recursive way. The decoding schedule of SC decoder includes two parts. One is to determine which of likelihood ratios (LRs) can be shared and which of LRs need to be calculated. The other is to select a calculation formula for a LR to be computed. As shown by the comparisons among the proposed method and existing methods, the proposed method solves the first part of the decoding schedule with constant critical path delay and lower space complexity, and solves the second part of the decoding schedule with less calculations. Besides, experimental results show that the proposed method does not affect the error performance of polar codes.

Most existing methods used in aeromagnetic compensation assume a constant geomagnetic field. These methods do not take into consideration the geomagnetic gradient, which can reduce the accuracy of the estimated coefficients of the Tolles-Lawson model. In practice, aside from the magnetic interference from the aircraft, a residual component of the geomagnetic field is contained in the filtered magnetic field signal. In this letter, the component of the geomagnetic field in the filtered magnetic field signal is modeled as a function of the longitude, latitude, and altitude of the aircraft. The linear equation for estimating the coefficients is then extended by combining the Tolles-Lawson model with the proposed model. Simulation results indicate that the proposed method is able to include the variance caused by the geomagnetic gradient into the Tolles-Lawson model calculation and results in a better aeromagnetic compensation.

In aeromagnetic compensation, the multicolinearity of the Tolles-Lawson model is one of the main factors limiting the performance of the coefficient-estimating methods. Aside from the intrinsic characteristic of the Tolles-Lawson model, data filtering as a preprocessing step in estimating the coefficients has a significant impact on the multicolinearity. To solve this problem, an adaptive filter based on wavelet multiresolution analysis is proposed. By modifying the wavelet decomposition results of the Mallat algorithms and reconstructing the signal, the adaptive filter can be used to establish a system of linear equations not only by capturing the frequency characteristics of the aircraft maneuvers as is usual but also by having a weakened multicolinearity. Simulation results illustrate the effectiveness of the proposed adaptive filter.

Reassembling fragmented image files plays a crucial role in seizing digital evidence from scattered digital image files. However, little attention has been paid to the quality assessment for reassembled image files. We propose a method to assign a reassembled image file with a score indicating its quality, defined as the amount of information that the reassembled image affords for its original image. The score ranges from 0 to 1. A higher score indicates better quality. The basic idea of the proposed method is to disintegrate the quality assessment of a reassembled image file into the calculations of the contributions of the fragments contained in the reassembled image file. For each fragment, if it is not from the original image file, it affords the least contribution. Otherwise its contribution depends on the extent to which the fragment and its neighbor fragments are concatenated correctly. We validate the performance of the proposed method with an extensive subjective study involving 588 reassembled image files. We also show that the existing quality assessments for images cannot be directly used to assess the quality of reassembled image files.

Due to its simplicity and effectiveness, copy-move forgery has become a commonly used approach to forging images and detection of this behavior has attracted much attention in recent years. Usually integrated in the copy-move forgery process is the rotation operation, which makes rotation invariance a critical property of the copy-move forgery detection methods. In this paper, we propose to use screened Harris Corner Detector and the MROGH descriptor as feature detector and descriptor respectively, to gain better feature coverage and robustness against rotation. Experimental results illustrates the efficacy of our method.

Figure-ground segmentation benefits lots of tasks in the field of computer vision. Exemplar-based approaches are capable of performing segmenting automatically without user interaction. However, most of them adopt fixed parameters for all the target images, which blocks their segmentation performances. We present a novel sparse representation based transfer scheme to gain adaptive parameters automatically. The proposed scheme transfers the segmentation masks of some windows from training images to obtain the soft mask of the target window from any given test image, when the target window can be represented by the linear combination of those windows. On the challenging PASCAL VOC 2010 segmentation dataset, experimental results and comparisons with the state-of-the-art methods show the effectiveness of the proposed scheme.

In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).

A novel multimodal biometric recognition algorithm based on complex kernel fisher discriminant analysis (complex KFDA) is proposed. Complex KFDA exploits two phases to generalize KFDA and perform classification for the fusion feature set: complex KPCA plus complex LDA. As two distinct biometric modals, the features of iris and face are fused in parallel to test our algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.

Robust hashing for multimedia authentication is an emerging research area. A novel key-dependent robust speech hashing based on speech production model is proposed in this letter. Robust hash is calculated based on linear spectrum frequencies (LSFs) which model the vocal tract. The correlation between LSFs is decoupled by discrete cosine transformation (DCT). A randomization scheme controlled by a secret key is applied in hash generation for random feature selection. The hash function is key-dependent and collision resistant. Meanwhile, it is highly robust to content preserving operations as well as having high accuracy of tampering localization.

A robust audio hashing algorithm for AAC audio is proposed in this paper. The robust hash is calculated from MDCT-coefficients which are derived by partial decoding of AAC bitstream. There is no complicated transformation in the proposed algorithm, therefore, it is of low computational complexity. The proposed method is highly robust to MDCT-based audio compression. Experimental results also show its high discrimination power and the robustness against content preserved operations.

Mobile agent technology is an attempt towards bringing the computation process nearer to the physical location of data in a distributed system. Although it has been extensively studied and applied in a variety of applications but still its usage is limited mainly because of the security issues associated with its usage. Mobile agent watermark encapsulation (MAWE) is a method that first embeds a watermark in the execution results of a mobile agent (MA) and then the watermark results are encapsulated using digital signature. Any malicious changes in the results can be easily tracked by the owner of the mobile agent. In this paper we discuss the attacks that can be launched against Mobile agent watermark encapsulation (MAWE) and how the security properties for mobile agent data integrity are fulfilled. We try to model the behavior of a malicious host by launching a series of attacks against mobile agent and then we see to what extent the security properties for MA can be achieved. The experiments performed in a typical ticket booking scenario suggest that MAWE can be used to protect the results of MA.

Numerous perceptual hashing algorithms have been developed for identification and verification of multimedia objects in recent years. Various application schemes have been adopted to provide the ability of forensic tracking against the illegal use or distribution of multimedia content. Developers and choice-maker are looking for a benchmark tool to compare and evaluate their ongoing algorithms or technologies. In this while, a novel benchmark platform is desired. PHABS provides an open framework to let its users define their own test strategy, perform tests, collect and analyze test data. With PHABS, various performances of algorithms can be tested, and different algorithms or algorithms with different parameters can be evaluated and compared easily.

In this paper, a perceptual audio hashing method in compressed domain is proposed for content identification, in which MDCT coefficients as the intermediate decoding result are selected for perceptual feature extraction and hash generation. The perceptual feature extraction is based on psychoacoustic model and exhibits good discrimination ability for different audio contents but robustness against common audio signal processing operations. Via feature extraction in the compressed domain, the MDCT-based compressed audios, such as MP3, AAC, etc., could be efficiently identified without complete decoding which facilitates those practical applications with strict requirements of memory and computational complexity, such as online audio retrieval, indexing of massive compressed audio data, audio identification by mobile phone, etc. The algorithm is highly robust against MDCT compression which is widely used in audio coding. Experiments demonstrate the effectiveness of the proposed scheme.

Reversible watermarking is suitable for hiding data in 2-D vector maps because the distortions induced by data embedding can be removed after extracting the hidden bits. In this paper, two reversible data-hiding schemes are explored based on the idea of difference expansion. The first scheme takes the coordinates of vertices as the cover data and hides data by modifying the differences between the adjacent coordinates. The scheme achieves high capacity in the maps with highly correlated coordinates. Instead of the raw coordinates, the second scheme adopts the manhattan distances between neighbor vertices as the cover data. A set of invertible integer mappings is defined to extract manhattan distances from coordinates and the hidden data are embedded by modifying the differences between the adjacent distances. For those maps where distances exhibit high correlation, this scheme shows better performance than the former one, both in capacity and invisibility. Three different maps with distinct features are used for the experiments. The results indicate that two proposed schemes suit different types of maps, respectively, according to the correlation of the selected cover data. Both schemes are strictly reversible. In addition, they can be slightly robust for low amplitude distortions by selecting higher digits for data hiding. The potential applications of proposed schemes may include map data authentication, secret communication, etc.

A novel statistic based algorithm for watermarking 2D vector maps is proposed. Unlike other multimedia data types, 2D vector map data has many special features, which make the selection of the cover data become a crucial issue directly influencing the performance of the watermarking scheme. In our proposed scheme, the selected cover data is a distance sequence extracted from the original map based on a feature point detecting procession. For a certain original map, this distance sequence can be regarded as a random variable with a stable distribution and the watermark is embedded by changing its distribution. Watermark extraction is based on statistic detection. Experiment results show that the proposed method has better shape-preserving performance, which is of great importance for vector maps. Moreover, this scheme is proved robust to certain attacks such as map interpolation, simplification, data reordering and some geometric transformations like rotation, translation, and their combination.

We present a reversible watermarking scheme which achieves perfect restoration of both the embedded watermark and the original image during extraction. The proposed scheme embeds data by modifying those integer DCT coefficients with peak amplitudes in each coefficient histogram. The integer DCT performed over the original image is a lossless 8/spl times/8 block transform with high energy concentrating ability, which guarantees reversibility and high capacity/distortion ratio for the proposed watermarking scheme. In addition, this scheme provides a wide quality (PSNR) range from around 40 dB to 60 dB for the watermarked image, and an inherent fine adjustment capability for the quality (PSNR). Some experimental results are presented to demonstrate the high performance of our scheme in terms of capacity and the quality of the watermarked image.