Copy–move forgery is one of the most preliminary and prevalent forms of modification attack on digital images. In this form of forgery, region(s) of an image is(are) copied and pasted onto itself, and subsequently the forged image is processed appropriately to hide the effects of forgery. State‐of‐the‐art copy–move forgery detection techniques for digital images are primarily motivated toward finding duplicate regions in an image. The last decade has seen lot of research advancement in the area of digital image forensics, whereby the investigation for possible forgeries is solely based on post‐processing of images. In this study, the authors present a three‐way classification of state‐of‐the‐art digital forensic techniques, along with a complete survey of their operating principles. In addition, they analyse the schemes and evaluate and compare their performances in terms of a proposed set of parameters, which may be used as a standard benchmark for evaluating the efficiency of any general copy–move forgery detection technique for digital images. The comparison results provided by them would help a user to select the most optimal forgery detection technique, depending on the author requirements.

Majority of the existing copy‐move forgery detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy‐move forgery using stationary wavelet transform (SWT) which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD) of an image. Also, the concept of colour‐based segmentation used in this work helps to achieve blur invariance. The authors’ experimental results prove the efficiency of the proposed method in detection of copy‐move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state‐of‐the‐art.

Whilst Model Based Testing (MBT) is an improvement over manual test specification, the leap from it to MBT can be hard. Only recently MBT tools for Web applications have emerged that can recover models from existing manually specified test cases. However, there are further requirements for supporting both MBT and manually specified tests. First, we need support for the generation of test initialization procedures. Also, we want to identify areas of the system that are not testable due to defects. We present Form Tester, a new MBT tool addressing these limitations. An evaluation with real Web applications shows that Form Tester helps to reduce the time spent on developing test cases.

Flexible link manipulators (FLMs) have several advantages over rigid link manipulators (RLMs) in aspects such as mass and moment of inertia, energy consumption, and speed. However, they suffer from a major limitation of flexibility that affects precision of operation. Control algorithms that are currently used for RLMs cannot be directly applied to FLMs. Majority of research on FLMs has focused on how to eliminate or reduce the undesirable effects of flexibility by specially developed control algorithms. In this paper, we introduce a new design change that stiffens the flexible link manipulator using cables and minimizes the effect of flexibility while retaining the advantages of FLMs. Such cable stiffened flexible link manipulators have the advantage of allowing the use of existing control techniques designed for RLMs. Effectiveness of this new approach is shown through simulations and experiments.