Block-based DCT Research Articles

Copy Move Forgery detection and localisation robust to rotation using block based Discrete Cosine Transform and eigenvalues

The contemporary era faces a widespread issue with digital image forgery, posing a significant challenge due to its ease and the broad reach enabled by high-speed internet. This manipulation of images carries substantial socio-political implications globally. Hence, robust digital image forensic methods are critical for detecting such forgeries. This article presents an innovative algorithm specifically designed to detect and locate copy-move duplication within digital images. By utilising the Discrete Cosine Transform and eigenvalues as distinguishing features, the algorithm precisely identifies and pinpoints replicated image regions from overlapping pixel blocks. Uniquely, another cumulative DCT feature enhances the algorithm’s ability to discern duplicated regions, even when subjected to post-processing rotation attacks. Experiments using various datasets demonstrate that this method outperforms avant-garde techniques in detecting and localising forgeries, showcasing promising results. This approach contributes significantly to the field of digital image forensics, providing a valuable tool for identifying and localising manipulated content.

Face Recognition Through Multi-Resolution Images

In this paper, we introduce a new method for face recognition in multi-resolution images. The proposed method is composed of two phases: an off-line phase and an inference phase. In the off-line phase, we built the Kernel Partial Least Squares (KPLS) regression model to map the LR facial features to HR ones. The KPLS predictor was then used in the inference phase to map HR features from LR features. We applied in both phases the Block-Based Discrete Cosine Transform (BBDCT) descriptor to enhance the facial feature description. Finally, the identity matching was carried out with the K-Nearest Neighbor (KNN) classifier. Experimental study was conducted on the AR and ORL databases and the obtained results proved the efficiency of the proposed method to deal with LR and VLR face recognition problem.

Abnormality Identification in Video Surveillance System using DCT

In the present world, video surveillance methods play a vital role in observing the activities that take place across secured and unsecured environment. The main aim with which a surveillance system is deployed is to spot abnormalities in specific areas like airport, military, forests and other remote areas, etc. A new block-based strategy is represented in this paper. This strategy is used to identify unusual circumstances by examining the pixel-wise frame movement instead of the standard object-based approaches. The density and also the speed of the movement is extorted by utilizing optical flow. The proposed strategy recognizes the unusual movement and differences by using discrete cosine transform coefficient. Our goal is to attain a trouble-free block-based Discrete Cosine Transform (DCT) strategy that promotes real-time abnormality detection. The proposed approach has been evaluated against an airport dataset and the outcome of unusual happenings occurred in is evaluated and reported.

Adaptive scaling factors based on the impact of selected DCT coefficients for image watermarking

Image watermarking technique aims at high imperceptibility of the embedded watermark with minimal distortion in the watermarked images. In frequency-domain, block-based Discrete Cosine Transform (DCT) is a popular method which can be improved by different scaling factors. This paper presents an adaptive scaling factor for selected DCT coefficients in image watermarking. The image blocks that have lowest pixel variances are chosen as the embedding locations. The optimal scaling factors for selected DCT coefficients on the middle frequencies are obtained by finding the best quality of images. The embedding process is performed using the obtained scaling factors. The proposed technique was investigated to verify the robustness and imperceptibility against various image-processing attacks. It is proved that our technique achieves higher robustness against noise addition, filter and compression than the existing schemes in most cases. Our technique produces greater imperceptibility than the existing schemes.

A Post-Processing Algorithm for Spatial Domain Detection & Removal of Alocking Artifacts

Block based Discrete Cosine Transform (BDCT) is commonly used to detect and remove blocking artifacts in the compressed images. We proposed spatial domain post processing algorithm with four fold model. In the initial stage, pixel vector (PV) is calculated for horizontal as well as vertical block boundaries, after defining PV calculation of different threshold values is made for extracting blocking artifacts. These thresholds are basically adaptive to the image quality due to strong correlation with the PV. To avoid ringing artifacts across block edges directional filter is proposed. Our research further worked on region classification based upon activity of PV within the blocks. Based upon different PV activity regions separate filters are used to achieve best filtering and finally Symmetrical Pixel Normalization filter (SPN Filter) is used to normalize the values of symmetrical pixel value for better visual performance . Proposed technique various indices like PSNR, MSSIM, GBIM are calculated and compare with different post processing techniques used in literature

Block DCT Based Optimization for Wireless SoftCast of Depth Map

With the recent emergence of naked-eye 3D mobile devices, the 3D video-based applications on the mobile terminal are showing great potential. However, the conventional transmission scheme based on separate source coding and channel coding may fail in the broadcast and mobile scenarios due to the drastic changes in wireless channel condition. Recently, an uncoded transmission scheme called SoftCast was introduced, which shows efficient performance for wireless 2D video transmission. However, it may not work well for 3D video wireless transmission, especially for the depth maps. Based on the piecewise smoothness of depth maps, we propose an uncoded wireless depth map transmission scheme, which incorporates three customized components of optimization into the SoftCast framework. First, compared with the frame-level discrete cosine transform (DCT) used in 2D video SoftCast, the mean removed block-based DCT is preferred as the decorrelation transform for depth maps to accommodate its piecewise smooth property, which also facilitates the following subsequent optimization. Second, view synthesis distortion-based chunk dropping is utilized for more efficient power allocation in the case of limited bandwidth, where the block DCT provides a better estimation for synthesis distortion. Third, inter-block DCT coefficients are rearranged to form chunks in terms of minimizing the intra-chunk variance, further enhancing the chunk-level power allocation efficiency. The experimental results demonstrate that the proposed scheme can provide superior performance than the state-of-the-art SoftCast methods while achieving graceful synthesis quality transition within a wide channel signal-to-noise ratio range.

An effective edge-preserving smoothing method for image manipulation

This paper presents a novel and effective edge-preserving image smoothing method for edge-aware image manipulation. The method formulates the smoothing as a problem of minimizing a convex object function with a constraint and an efficient solution to the optimization problem is presented. Specifically, the method provides an unified framework to regularize the edge and texture pixels in the optimization so that geometric edges representing image structures can be well retained and fine edges of texture regions are removed or suppressed. Both qualitative and quantitative experimental results on natural images and computer-generated structured images have shown the efficacy of the proposed method. In addition, the proposed method can improve the performance of many image processing and manipulation tasks including edge extraction and simplification, non-photorealistic rendering, detail and contrast exaggeration, HDR tone mapping, block-based discrete cosine transform (BDCT) artifact removal and content-aware image resizing, as demonstrated through the experiments.

A Secure and Robust Image Watermarking System Using Normalization and Arnold Scrambling

This article proposes an image watermarking scheme to improve the robustness and security of watermark against several attacks. To achieve this, image normalization is utilized where affine transformation is applied on the image and which makes it as invariant to geometric trans- formations. The Lifting Wavelet Transform (LWT) and block based Discrete Cosine Transform (DCT) is applied to the cover image after normalizing an image. Then, the DC coefficients from all blocks are gathered and singular value matrix is constructed using Singular Value Decomposition (SVD). The watermark image is embedded in this singular value matrix after scrambling the image, which increases the security of the proposed scheme. The robustness and invisibleness of the proposed scheme is measured using Peak Signal to Noise Ratio (PSNR), Signal to noise Ratio (SNR) and Structural Similarity (SSIM). The comparison was made with existing schemes and it reveals that the proposed scheme provides high robustness than the existing one.

Robust and blind watermarking technique in DCT domain using inter-block coefficient differencing

In this paper, a robust blind watermarking technique, based on block-based DCT coefficient modification is proposed. The difference between two DCT coefficients of the adjacent blocks at the same position is calculated. Depending upon the watermark bit to be embedded; this difference is brought in a particular predefined range which is achieved by modifying one of the two DCT coefficients. The amount of modification to a DCT coefficient of a block depends upon the scaling variable, DC coefficient and median of certain zig-zag ordered AC coefficients of that block. The robustness of the proposed scheme has been examined for various singular and hybrid attacks. Comparison results reveal that the proposed technique has a higher degree of robustness against various singular and hybrid attacks. Further a watermark of good quality is extracted even after various simultaneous attacks on the system.

Image Deblocking Scheme for JPEG Compressed Images Using an Adaptive-Weighted Bilateral Filter

Due to the block-based discrete cosine transform (BDCT), JPEG compressed images usually exhibit blocking artifacts. When the bit rates are very low, blocking artifacts will seriously affect the image`s visual quality. A bilateral filter has the features for edge-preserving when it smooths images, so we propose an adaptiveweighted bilateral filter based on the features. In this paper, an image-deblocking scheme using this kind of adaptive-weighted bilateral filter is proposed to remove and reduce blocking artifacts. Two parameters of the proposed adaptive-weighted bilateral filter are adaptive-weighted so that it can avoid over-blurring unsmooth regions while eliminating blocking artifacts in smooth regions. This is achieved in two aspects: by using local entropy to control the level of filtering of each single pixel point within the image, and by using an improved blind image quality assessment (BIQA) to control the strength of filtering different images whose blocking artifacts are different. It is proved by our experimental results that our proposed image-deblocking scheme provides good performance on eliminating blocking artifacts and can avoid the over-blurring of unsmooth regions.

English

Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms.

Information hiding in medical images: a robust medical image watermarking system for E-healthcare

Electronic transmission of the medical images is one of the primary requirements in a typical Electronic-Healthcare (E-Healthcare) system. However this transmission could be liable to hackers who may modify the whole medical image or only a part of it during transit. To guarantee the integrity of a medical image, digital watermarking is being used. This paper presents two different watermarking algorithms for medical images in transform domain. In first technique, a digital watermark and Electronic Patients Record (EPR) have been embedded in both regions; Region of Interest (ROI) and Region of Non-Interest (RONI). In second technique, Region of Interest (ROI) is kept untouched for tele-diagnosis purpose and Region of Non-Interest (RONI) is used to hide the digital watermark and EPR. In either algorithm 8ź×ź8 block based Discrete Cosine Transform (DCT) has been used. In each 8ź×ź8 block two DCT coefficients are selected and their magnitudes are compared for embedding the watermark/EPR. The selected coefficients are modified by using a threshold for embedding bit a `0' or bit `1' of the watermark/EPR. The proposed techniques have been found robust not only to singular attacks but also to hybrid attacks. Comparison results viz-a - viz payload and robustness show that the proposed techniques perform better than some existing state of art techniques. As such the proposed algorithms could be useful for e-healthcare systems.

Non-stationary feature fusion of face and palmprint multimodal biometrics

Multimodal biometrics provides high performance in biometric recognition systems with respect to unibiometric systems as they offer a more universal approach, added security and better recognition accuracy. Moreover, data acquisition at the feature level brings out rich information from the traits, thus fusion of modalities at this level is desirable. In this paper we propose a novel fusion technique called non-stationary feature fusion where a new structure of interleaved matrix is constructed using local features extracted from two modalities i.e. face and palmprint images. A block based Discrete Cosine Transform (DCT) algorithm is used to construct a fused feature vector by extracting independent feature vectors from each spatial image. This fused feature vector contains nonlinear information that is used to train a Gaussian Mixture Models (GMM) based statistical model. The model provides accurate estimation of the class conditional probability density function of the fused feature vector. The proposed method produces recognition rates as high as 99.7% and 97% when tested on benchmark databases-ORL-PolyU and FERET-PolyU respectively. These rates are achieved using 23% low frequency DCT coefficients. The new technique is shown to outperform existing feature level fusion methods including methods based on matching and decision level fusion.

All Phase Biorthogonal Transform Based on GPU

In this paper, all phase biorthogonal transform (APBT) based on parallel algorithm is proposed. It can solve two problems. First, block-based DCT transform coding has serious blocking artifacts when the image is highly compressed at low bit rate. Second, APBT can solve the problem about blocking artifacts, but it does not have a fast algorithm, it has a low efficiency when APBT applies to image processing. So APBT based on parallel algorithm can solve the above problems, and it provides more space for improving the processing speed of APBT. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of APBT. Experimental results show that the maximum speedup ratio of parallel algorithm of APBT can reach more than 40 times with a very low version GPU, compared with conventional serial APBT. And the reconstructed image using the proposed algorithm has the same performance with the serial one in terms of objective quality and subjective effect. The proposed parallel algorithm based on GPU of APBT also can be used in image compression, video compression, the edge detection, and some other fields of image processing.

APBT-JPEG Image Coding Based on GPU

In wireless multimedia sensor networks (WMSN), the latency of transmission is an increasingly problem. With the improvement of resolution, the time cost in image and video compression is more and more, which seriously affects the real-time of WMSN. In JPEG system, the core of the system is DCT, but DCT-JPEG is not the best choice. Block-based DCT transform coding has serious blocking artifacts when the image is highly compressed at low bit rates. APBT is used in this paper to solve that problem, but APBT does not have a fast algorithm. In this paper, we analyze the structure in JPEG and propose a parallel framework to speed up the algorithm of JPEG on GPU. And we use all phase biorthogonal transform (APBT) to replace the discrete cosine transform (DCT) for the better performance of reconstructed image. Therefore, parallel APBT-JPEG is proposed to solve the real-time of WMSN and the blocking artifacts in DCT-JPEG in this paper. We use the CUDA toolkit based on GPU which is released by NVIDIA to design the parallel algorithm of APBT-JPEG. Experimental results show that the maximum speedup ratio of parallel algorithm of APBT-JPEG can reach more than 100 times with a very low version GPU, compared with conventional serial APBT-JPEG. And the reconstructed image using the proposed algorithm has better performance than the DCT-JPEG in terms of objective quality and subjective effect. The proposed parallel algorithm based on GPU of APBT also can be used in image compression, video compression, the edge detection and some other fields of image processing.

An efficient spatial deblocking of images with DCT compression

We propose an adaptive spatial postprocessing algorithm for using in block-based discrete cosine transform (BDCT) coded images. The proposed method is comprised of three procedural steps: a thresholding step, a model classification step, and a deblocking filtering step. First, we apply adaptive thresholding to extract the pixel vector containing the blocking artifacts. This threshold has a strong correlation with image quality and the standard deviation of the pixel vector. Next, we update block types using a simple rule, thus influencing deblocking performance. Our research proposes that the activity of the pixel vector can be used to categorize the pixel vector model. With the pixel vector model determined, we are then able to apply a suitable filter to each model with different local properties. For example, we are able to utilize a directional filter to reduce ringing artifacts in edge regions. With various images and bit-rate conditions, images deblocked by the proposed method exhibit both significant visual quality improvement and PSNR gain, with fairly low computational complexity.

Copy-Move Forgery Detection using DCT and SIFT

Digital images are the most prevalent way to spread a message. So the authenticity of images is very essential. But due to advancement of the technology the editing of images has become very effortless. Copy-move forgery is most basic technique to alter an image. In this one part of image is copied, called as snippet, and pasted within same image and most likely post-processing it. Considerable number of algorithms is proposed to detect different post-processing on snippet of image. In this paper novel approach is proposed to detect combination of different post-processing operations by single method. It is analyzed that block-based features method DCT is robust to Gaussian noise and JPEG compression, secondly the keypoint-based feature method SIFT is robust to rotation and scaling. Thus by combining SIFT and DCT we are able to detect forgery under post-processing operations of rotation, scaling, Gaussian noise, and JPEG compression and thus the efficiency to detect forgery improves.

An Efficient Hand-Based Biometric Recognition System Using Finger- Knuckle-Print Data

Automatic personal identification is playing an important role in secure and reliable applications, such as access control, surveillance systems, information systems, physical buildings and many more applications. In contrast with traditional approaches, based on what a person knows (password) or what a person has (tokens), biometric based identification is providing an improved security for their users. Biometrics is the measurement of physiological traits such as palmprints, fingerprints, iris etc., and/or behavioral traits such as gait, signature etc., of an individual person for personal recognition. Hand-based person identification provides a good user acceptance, distinctiveness, universality, relatively easy to capture and low-cost. However, Finger-Knuckle-Print (FKP), which provides different information from a variety of finger types, has been recently used to improve the performance of hand-based biometric identification because each finger has a specific feature, making it possible to collect more information to improve the accuracy of hand-based biometric systems. In this paper, we presented an efficient online personal identification based on FKP using the twodimensional Block based Discrete Cosine Transform (2D-BDCT) and MultiVariate Normal density function (MVN). In this study, a segmented FKP is firstly divided into non-overlapping and equal-sized blocks, and then, applies the 2DBDCT over each block. By using zig-zag scan order, each transform block is reordered to produce the feature vector. Subsequently, we use the MVN for modeling the feature vector of each FKP. Finally, Log-likelihood scores are used for FKP matching. Finally, performance of all finger types is determined individually and several fusion rules are applied to develop a multimodal system based on fusion at the matching score level. Experimental results show that FKPs modalities show best performance for identifying a person as they provide an excellent identification rate and with more security. Here we also discuss few patents that are relevant to the article.

Design and Development of Ant Colony System Algorithms for Block-Based DCT Watermarking

Design and Development of Ant Colony System Algorithms for Block-Based DCT Watermarking

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.