Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection

Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.Comment: 13 pages, 11 figure

Numerical Simulation and Design of Copy Move Image Forgery Detection Using ORB and K Means Algorithm

Copy-move is a common technique for tampering with images in the digital realm. Therefore, image security authentication is of critical importance in our society. So copy move forgery detection (CMFD) is activated in order to identify the forged portion of a photograph. A combination of the Scaled ORB and the k-means++ algorithm is used to identify this object. The first step is to identify the space on a pyramid scale, which is critical for the next step. A region's defining feature is critical to its detection. Because of this, the ORB descriptor plays an important role. Extracting FAST key points and ORB features from each scale space. The coordinates of the FAST key points have been reversed in relation to the original image. The ORB descriptors are now subjected to the k-means++ algorithm. Hammering distance is used to match the clustered features every two key points. Then, the forged key points are discovered. This information is used to draw two circles on the forged and original regions. Moment must be calculated if the forged region is rotational invariant. Geometric transformation (scaling and rotation) is possible in this method. For images that have been rotated and smoothed, this work demonstrates a method for detecting the forged region. The running time of the proposed method is less than that of the previous method

Detection of intentionally made changes in image content

Digital images and video signals represent the most frequently transmitted contents. Namely, with the development of modern digital cameras and smartphones, the use of multimedia content increases every day. They are used in everyday life, for getting information and also as authenticated proofs or corroboratory evidence in different areas like: forensic studies, law enforcement, journalism and others...Multifraktalna analiza se pokazala kao dobar alat za analizu postojećih slika, kao i segmentaciju određenih regiona, izdvajanje ivica, uglova slike i slično. Kako kopirani i nalepljeni delovi imaju sličnu strukturu, može se primeniti multifraktalna analiza, koja u osnovi analizira samosličnost. Multifraktalni spektar daje globalni opis slike (ili, opštije, fenomena koji se ispituje). Vrednost Hölder-ovog eksponenta zavisi od položaja u strukturi i opisuje lokalnu regularnost signala. Naime, različiti objekti na slici imaju različite spektre, različite pozicije maksimuma, minimuma, prve nule itd, što se pokazalo kao interesantan skup različitih parametara pomoću kojih se mogu detektovati namerne promene na slikama..

Copy-move forgery detection in digital images

The ready availability of image-editing software makes it important to ensure the authenticity of images. This thesis concerns the detection and localization of cloning, or Copy-Move Forgery (CMF), which is the most common type of image tampering, in which part(s) of the image are copied and pasted back somewhere else in the same image. Post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of detecting forgery. This thesis presents three novel methods for CMF detection, using feature extraction, surface fitting and segmentation. The Dense Scale Invariant Feature Transform (DSIFT) has been improved by using a different method to estimate the canonical orientation of each circular block. The Fitting Function Rotation Invariant Descriptor (FFRID) has been developed by using the least squares method to fit the parameters of a quadratic function on each block curvatures. In the segmentation approach, three different methods were tested: the SLIC superpixels, the Bag of Words Image and the Rolling Guidance filter with the multi-thresholding method. We also developed the Segment Gradient Orientation Histogram (SGOH) to describe the gradient of irregularly shaped blocks (segments). The experimental results illustrate that our proposed algorithms can detect forgery in images containing copy-move objects with different types of transformation (translation, rotation, scaling, distortion and combined transformation). Moreover, the proposed methods are robust to post-processing (i.e. blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise) and can detect multiple duplicated objects. In addition, we developed a new method to estimate the similarity threshold for each image by optimizing a cost function based probability distribution. This method can detect CMF better than using a fixed threshold for all the test images, because our proposed method reduces the false positive and the time required to estimate one threshold for different images in the dataset. Finally, we used the hysteresis to decrease the number of false matches and produce the best possible result

Copy-move forgery detection: a survey on time complexity issues and solutions

As the image processing especially image editing software evolve, more image manipulations were possible to be done, thus authentication of image become a very crucial task. Copy-move forgery detection (CMFD), a popular research focus in digital image forensic, is used to authenticate an image by detecting malicious copy-move tampering in an image. Copy-move forgery occurs when a region in an image is copied and paste into the same image. There were many survey and review papers discussed about CMFD robustness and accuracy yet less attention was given to performance and time complexity. In this paper, we attempts to highlight the key factors contribute to the time complexity issue. Before that, the CMFD processes were first explained for better understanding. The trends of tackling those issues are then explored. Finally, numbers of proposed solutions will be outlined to conclude this paper

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique

In this thesis, the effect of feature type and matching method has been analyzed by comparing different combinations of matching method – feature type for copy-move image forgery detection. The results showed an interaction between some of the features and some of the matching methods. Due to the importance of matching process, this thesis focused on improving the matching process by proposing an enhanced block-based copy-move forgery detection pipeline. The proposed pipeline relied on clustering the image blocks into clusters, and then independently performing the matching of the blocks within each cluster which will reduce the time required for matching and increase the true positive ratio (TPR) as well. In order to deploy the proposed pipeline, two combinations of matching method - feature type are considered. In the first case, Zernike Moments (ZMs) were combined with Locality Sensitive Hashing (LSH) and tested on three datasets. The experimental results showed that the proposed pipeline reduced the processing time by 73.05% to 84.70% and enhanced the accuracy of detection by 5.56% to 25.43%. In the second case, Polar Cosine Transform (PCT) was combined with Lexicographical Sort (LS). Although the proposed pipeline could not reduce the processing time, it enhanced the accuracy of detection by 32.46%. The obtained results were statistically analyzed, and it was proven that the proposed pipeline can enhance the accuracy of detection significantly based on the comparison with other two methods

A Holmes and Doyle Bibliography, Volume 9: All Formats—Combined Alphabetical Listing

This bibliography is a work in progress. It attempts to update Ronald B. De Waal’s comprehensive bibliography, The Universal Sherlock Holmes, but does not claim to be exhaustive in content. New works are continually discovered and added to this bibliography. Readers and researchers are invited to suggest additional content. This volume contains all listings in all formats, arranged alphabetically by author or main entry. In other words, it combines the listings from Volume 1 (Monograph and Serial Titles), Volume 3 (Periodical Articles), and Volume 7 (Audio/Visual Materials) into a comprehensive bibliography. (There may be additional materials included in this list, e.g. duplicate items and items not yet fully edited.) As in the other volumes, coverage of this material begins around 1994, the final year covered by De Waal's bibliography, but may not yet be totally up-to-date (given the ongoing nature of this bibliography). It is hoped that other titles will be added at a later date. At present, this bibliography includes 12,594 items