Reversible color video watermarking scheme based on hybrid of integer-to-integer wavelet transform and Arnold transform

Unauthorized redistribution and illegal copying of digital contents are serious issues which have affected numerous types of digital contents such as digital video. One of the methods, which have been suggested to support copyright protection, is to hide digital watermark within the digital video. This paper introduces a new video watermarking system which based on a combination of Arnold transform and integer wavelet transforms (IWT). IWT is employed to decompose the cover video frames whereby Arnold transform is used to scramble the watermark which is a grey scale image. Scrambling the watermark before the concealment makes the transmission more secure by disordering the information. The system performance was benchmarked against related video watermarking schemes, in which the evaluation processes consist of testing against several video operations and attacks. Consequently, the scheme has been demonstrated to be perfectly robust

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Contourlet Domain Image Modeling and its Applications in Watermarking and Denoising

Statistical image modeling in sparse domain has recently attracted a great deal of research interest. Contourlet transform as a two-dimensional transform with multiscale and multi-directional properties is known to effectively capture the smooth contours and geometrical structures in images. The objective of this thesis is to study the statistical properties of the contourlet coefficients of images and develop statistically-based image denoising and watermarking schemes. Through an experimental investigation, it is first established that the distributions of the contourlet subband coefficients of natural images are significantly non-Gaussian with heavy-tails and they can be best described by the heavy-tailed statistical distributions, such as the alpha-stable family of distributions. It is shown that the univariate members of this family are capable of accurately fitting the marginal distributions of the empirical data and that the bivariate members can accurately characterize the inter-scale dependencies of the contourlet coefficients of an image. Based on the modeling results, a new method in image denoising in the contourlet domain is proposed. The Bayesian maximum a posteriori and minimum mean absolute error estimators are developed to determine the noise-free contourlet coefficients of grayscale and color images. Extensive experiments are conducted using a wide variety of images from a number of databases to evaluate the performance of the proposed image denoising scheme and to compare it with that of other existing schemes. It is shown that the proposed denoising scheme based on the alpha-stable distributions outperforms these other methods in terms of the peak signal-to-noise ratio and mean structural similarity index, as well as in terms of visual quality of the denoised images. The alpha-stable model is also used in developing new multiplicative watermark schemes for grayscale and color images. Closed-form expressions are derived for the log-likelihood-based multiplicative watermark detection algorithm for grayscale images using the univariate and bivariate Cauchy members of the alpha-stable family. A multiplicative multichannel watermark detector is also designed for color images using the multivariate Cauchy distribution. Simulation results demonstrate not only the effectiveness of the proposed image watermarking schemes in terms of the invisibility of the watermark, but also the superiority of the watermark detectors in providing detection rates higher than that of the state-of-the-art schemes even for the watermarked images undergone various kinds of attacks

A review on copy-move image forgery detection techniques

With billions of digital images flooding the internet which are widely used and regards as the major information source in many fields in recent years. With the high advance of technology, it may seem easy to fraud the image. In digital images, copy-move forgery is the most common image tampering, where some object(s) or region(s) duplicate in the digital image. The important research has attracted more attention in digital forensic is forgery detection and localization. Many techniques have been proposed and many papers have been published to detect image forgery. This paper introduced a review of research papers on copy-move image forgery published in reputed journals from 2017 to 2020 and focused on discussing various strategies related with fraud images to highlight on the latest tools used in the detection. This article will help the researchers to understand the current algorithms and techniques in this field and ultimately develop new and more efficient algorithms of detection copy-move image

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Image splicing detection scheme using adaptive threshold mean ternary pattern descriptor

The rapid growth of image editing applications has an impact on image forgery cases. Image forgery is a big challenge in authentic image identification. Images can be readily altered using post-processing effects, such as blurring shallow depth, JPEG compression, homogenous regions, and noise to forge the image. Besides, the process can be applied in the spliced image to produce a composite image. Thus, there is a need to develop a scheme of image forgery detection for image splicing. In this research, suitable features of the descriptors for the detection of spliced forgery are defined. These features will reduce the impact of blurring shallow depth, homogenous area, and noise attacks to improve the accuracy. Therefore, a technique to detect forgery at the image level of the image splicing was designed and developed. At this level, the technique involves four important steps. Firstly, convert colour image to three colour channels followed by partition of image into overlapping block and each block is partitioned into non-overlapping cells. Next, Adaptive Thresholding Mean Ternary Pattern Descriptor (ATMTP) is applied on each cell to produce six ATMTP codes and finally, the tested image is classified. In the next part of the scheme, detected forgery object in the spliced image involves five major steps. Initially, similarity among every neighbouring district is computed and the two most comparable areas are assembled together to the point that the entire picture turns into a single area. Secondly, merge similar regions according to specific state, which satisfies the condition of fewer than four pixels between similar regions that lead to obtaining the desired regions to represent objects that exist in the spliced image. Thirdly, select random blocks from the edge of the binary image based on the binary mask. Fourthly, for each block, the Gabor Filter feature is extracted to assess the edges extracted of the segmented image. Finally, the Support Vector Machine (SVM) is used to classify the images. Evaluation of the scheme was experimented using three sets of standard datasets, namely, the Institute of Automation, Chinese Academy of Sciences (CASIA) version TIDE 1.0 and 2.0, and Columbia University. The results showed that, the ATMTP achieved higher accuracy of 98.95%, 99.03% and 99.17% respectively for each set of datasets. Therefore, the findings of this research has proven the significant contribution of the scheme in improving image forgery detection. It is recommended that the scheme be further improved in the future by considering geometrical perspective

Tamper detection of qur'anic text watermarking scheme based on vowel letters with Kashida using exclusive-or and queueing technique

The most sensitive Arabic text available online is the digital Holy Qur’an. This sacred Islamic religious book is recited by all Muslims worldwide including the non-Arabs as part of their worship needs. It should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of the Arabic letters like the vowels ( أ . و . ي ), Kashida (extended letters), and other symbols in the Holy Qur’an must be secured from alterations. The cover text of the al-Qur’an and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR), Embedding Ratio (ER), and Normalized Cross-Correlation (NCC), thus the location for tamper detection gets low accuracy. Watermarking technique with enhanced attributes must therefore be designed for the Qur’an text using Arabic vowel letters with Kashida. Most of the existing detection methods that tried to achieve accurate results related to the tampered Qur’an text often show various limitations like diacritics, alif mad surah, double space, separate shapes of Arabic letters, and Kashida. The gap addressed by this research is to improve the security of Arabic text in the Holy Qur’an by using vowel letters with Kashida. The purpose of this research is to enhance Quran text watermarking scheme based on exclusive-or and reversing with queueing techniques. The methodology consists of four phases. The first phase is pre-processing followed by the embedding process phase to hide the data after the vowel letters wherein if the secret bit is ‘1’, insert the Kashida but do not insert it if the bit is ‘0’. The third phase is extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR (for the imperceptibility), ER (for the capacity), and NCC (for the security of the watermarking). The experimental results revealed the improvement of the NCC by 1.77 %, PSNR by 9.6 %, and ER by 8.6 % compared to available current schemes. Hence, it can be concluded that the proposed scheme has the ability to detect the location of tampering accurately for attacks of insertion, deletion, and reordering