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Digital images have numerous applications in various fields such as cyber forensics, courts, newspapers, magazines, medical field and many more. Therefore, the trustfulness of the contents of an image is very essential. Image manipulation has become very much simple and common due to the development of high resolution digital cameras, powerful computers and numerous image editing software. Hence, falsification of visual content of images is not at all limited to specialists and detection of forged images have become a highly challenging task. Of the different kinds of image forgery, the proposed method deals with one particular kind known as copy-move forgery. The method is an integration of the conventional block-based and keypoint based methods. The method first identifies the forged keypoint locations and then localizes the forged region using a region extraction technique. To improve the accuracy of detection, a Binary Discriminative Feature descriptor (BDF) is used for feature detection and matching. The suspected regions are identified by replacing the matching feature points with corresponding superpixel blocks. The neighbouring blocks are then merged with the suspected regions based on color similarity, using Color Histogram Matching and a final morphological close operation extracts the detected forged regions. Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy-move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring.

Object tracking relies on a recursive search technique around the previous target location, concurrently learning the target appearance in each frame. A failure in any frame causes a drift from its optimal target path. Thus, obtaining highly confident search regions is essential in each frame. Motivated by the strong localization property of segmented object masks, the proposed method introduces instance segmentation as an attention mechanism in object tracking framework. The core contribution of this paper is threefold: (i) a region proposal module (RPM) based on instance segmentation to focus on search proposals having a high probability of being the target, (ii) a target localization module (TLM) to localize the final target using a correlation filter and (iii) a domain adaptation technique in both RPM and TLM modules to incorporate target specific knowledge and strong discrimination ability. Extensive experimental evaluation on three benchmark datasets demonstrate a significant average gain of 2.47% in precision, 2.55% in AUC score and 2.15% in overlap score in comparison with recent competing trackers.

Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically changing object appearance. In order to tackle such challenges, the existing strategies often rely on a particular set of algorithms. Here, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map. Further, we demonstrate the impact of displacement consistency on CF trackers. The generality and efficiency of the proposed framework is illustrated by integrating our contributions into two state-of-the-art CF trackers: SRDCF and ECO. As per the comprehensive experiments on the VOT2016 dataset, our top trackers show substantial improvement of \(14.7\%\) and \(6.41\%\) in robustness, \(11.4\%\) and \(1.71\%\) in Average Expected Overlap (AEO) over the baseline SRDCF and ECO, respectively.

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net). Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua

Accurate and robust visual object tracking is one of the most challenging computer vision problems. Recently, discriminative correlation filter trackers have shown promising results on benchmark datasets with continuous performance improvements in tracking accuracy and robustness. Still, these algorithms fail to track as the target object and background conditions undergo drastic changes over time. They are also incapable to resume tracking once the target is lost, limiting the ability to track long term. The proposed BoVW-CFT is a classifier-based generic technique to handle tracking uncertainties in correlation filter trackers. Tracking failures in correlation trackers are automatically identified and an image classifier with training, testing and online update stages is proposed as detector in the tracking scenario using Bag of Visual Words (BoVW) features. The proposed detector falls under the parts based model and is quite well suited in the tracking framework. Further, the online training stage in the proposed framework with updated model or training samples, incorporates temporal information, helping to detect rotated, blurred and scaled versions of the target. On detecting a target loss in the correlation tracker, the trained classifier, referred to as detector, is invoked to re-initialize the tracker with the actual target location. Therefore, for each tracking uncertainty, two output patches are obtained, one each from the base tracker and the classifier. The final target location is estimated using the normalized cross-correlation with the initial target patch. The method has the advantages of mitigating the model drift in correlation trackers and learns a robust model that tracks long term. Extensive experimental results demonstrate an improvement of 4.1% in the expected overlap, 1.86% in accuracy and 15.46% in robustness on VOT2016 and 1.82% in overlap precision, 2.32% in AUC and 2.87% in success rates on OTB100.