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Digital image authenticity is always an imperative question to tackle whenever a digital image is being assessed for its content. Using digital forensic algorithms, the image will be evaluated for various traces left from numerous categories of manipulations including, among others, copy-move operations. Later this is considered an essential block in most digital image forgeries. It results in changing the information incorporated in a scene, hiding information from an image, or emphasizing some parts of the image. In this paper we propose and investigate two main approaches that differ in the feature extraction process in order to detect copy-move traces. In the first method, we use two-dimensional discrete cosine transform. Whereas in the second method, the phase response of Gabor filter is being used. Instead of being applied on the image directly, the two methods are applied over the first, the second or the third principal component of the image after being divided into overlapping blocks. Combining these conditions results in six basic implementations that are investigated under three parameters that must be optimized: block dimension, contrast and similarity thresholds. Results from testing and validation process demonstrate that the highest performance, in terms of false accept rate, is obtained when using Gabor filter associated with the first principal component of the image outperforming a reference method we implemented as well. [ABSTRACT FROM AUTHOR]

In this work, we propose a technique that utilizes a fully convolutional network (FCN) to localize image splicing attacks. We first evaluated a single-task FCN (SFCN) trained only on the surface label. Although the SFCN is shown to provide superior performance over existing methods, it still provides a coarse localization output in certain cases. Therefore, we propose the use of a multi-task FCN (MFCN) that utilizes two output branches for multi-task learning. One branch is used to learn the surface label, while the other branch is used to learn the edge or boundary of the spliced region. We trained the networks using the CASIA v2.0 dataset, and tested the trained models on the CASIA v1.0, Columbia Uncompressed, Carvalho, and the DARPA/NIST Nimble Challenge 2016 SCI datasets. Experiments show that the SFCN and MFCN outperform existing splicing localization algorithms, and that the MFCN can achieve finer localization than the SFCN. [ABSTRACT FROM AUTHOR]

Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques. [ABSTRACT FROM AUTHOR]

Due to the wide diffusion of JPEG coding standard, the image forensic community has devoted significant attention to the development of double JPEG (DJPEG) compression detectors through the years. The ability of detecting whether an image has been compressed twice provides paramount information toward image authenticity assessment. Given the trend recently gained by convolutional neural networks (CNN) in many computer vision tasks, in this paper we propose to use CNNs for aligned and non-aligned double JPEG compression detection. In particular, we explore the capability of CNNs to capture DJPEG artifacts directly from images. Results show that the proposed CNN-based detectors achieve good performance even with small size images (i.e., 64 × 64), outperforming state-of-the-art solutions, especially in the non-aligned case. Besides, good results are also achieved in the commonly-recognized challenging case in which the first quality factor is larger than the second one. [ABSTRACT FROM AUTHOR]

The article tips for school librarians to enhance the skills of students on determining the information quality. Among the suggestions includes distinguishing fact and opinion, identifying fake images, and questioning the sponsorship. Also include the application of triangulation, preselection of quality sources, and building effective lessons.

Advances in digital technology mean that the creation of visually compelling photographic fakes is growing at an incredible speed. The prevalence of manipulated photos in our everyday lives invites an important, yet largely unanswered, question: Can people detect photo forgeries? Previous research using simple computer-generated stimuli suggests people are poor at detecting geometrical inconsistencies within a scene. We do not know, however, whether such limitations also apply to real-world scenes that contain common properties that the human visual system is attuned to processing. In two experiments we asked people to detect and locate manipulations within images of real-world scenes. Subjects demonstrated a limited ability to detect original and manipulated images. Furthermore, across both experiments, even when subjects correctly detected manipulated images, they were often unable to locate the manipulation. People's ability to detect manipulated images was positively correlated with the extent of disruption to the underlying structure of the pixels in the photo. We also explored whether manipulation type and individual differences were associated with people's ability to identify manipulations. Taken together, our findings show, for the first time, that people have poor ability to identify whether a real-world image is original or has been manipulated. The results have implications for professionals working with digital images in legal, media, and other domains. [ABSTRACT FROM AUTHOR]

Copy-move forgery (CMF) is a popular image manipulation technique that is simple and effective in creating forged illustrations. The bulk of CMF detection methods concentrate on common geometrical transformation attacks (e.g., rotation and scale) and post-processing attacks (e.g., Joint Photographic Experts Group (JPEG) compression and Gaussian noise addition). However, geometrical transformation that involves reflection attacks has not yet been highlighted in the literature. As the threats of reflection attack are inevitable, there is an urgent need to study CMF detection methods that are robust against this type of attack. In this study, we investigated common geometrical transformation attacks and reflection-based attacks. Also, we suggested a robust CMF detection method, called SIFT-Symmetry, that innovatively combines the Scale Invariant Feature Transform (SIFT)-based CMF detection method with symmetry-based matching. We evaluated the SIFT-Symmetry with three established methods that are based on SIFT, multi-scale analysis, and patch matching using two new datasets that cover simple transformation and reflection-based attacks. The results show that the F-score of the SIFT-Symmetry method surpassed the average 80% value for all geometrical transformation cases, including simple transformation and reflection-based attacks, except for the reflection with rotation case which had an average F-score of 65.3%. The results therefore show that the SIFT-Symmetry method gives better performance compared to the other existing methods. [ABSTRACT FROM AUTHOR]

The detection of faked identities is a major problem in security. Current memory-detection techniques cannot be used as they require prior knowledge of the respondent’s true identity. Here, we report a novel technique for detecting faked identities based on the use of unexpected questions that may be used to check the respondent identity without any prior autobiographical information. While truth-tellers respond automatically to unexpected questions, liars have to “build” and verify their responses. This lack of automaticity is reflected in the mouse movements used to record the responses as well as in the number of errors. Responses to unexpected questions are compared to responses to expected and control questions (i.e., questions to which a liar also must respond truthfully). Parameters that encode mouse movement were analyzed using machine learning classifiers and the results indicate that the mouse trajectories and errors on unexpected questions efficiently distinguish liars from truth-tellers. Furthermore, we showed that liars may be identified also when they are responding truthfully. Unexpected questions combined with the analysis of mouse movement may efficiently spot participants with faked identities without the need for any prior information on the examinee. [ABSTRACT FROM AUTHOR]

Picture imitation discovery expects to confirm the genuineness of a computerized picture. Picture validation arrangement is grouped into two sorts. A dynamic phony identification systems, for example, advanced watermarking or computerized marks utilizes a known validation code inserted into the picture content before the pictures are sent through an untrustworthy open channel. By confirming the nearness of such validation code confirmation might be demonstrated by contrasting and the first embedded code. Be that as it may, this technique requires uncommon equipment or programming to embed the verification code inside the picture before the picture is being circulated. Uninvolved or daze phony discovery procedure utilizes the got picture just to assess its legitimacy or uprightness, with no signature or watermark of the first picture from the sender. It depends on the presumption that albeit advanced fabrications may leave no visual pieces of information of having been messed with, they may very likely irritate the hidden measurements property or picture consistency of a characteristic scene picture which presents new antiquities bringing about different types of irregularities. These irregularities can be utilized to recognize the phony. This procedure is mainstream as it needn't bother with any earlier data about the picture. Existing systems recognize different hints of altering and identify them independently with confinement of altered locale. Duplicate move is the most widely recognized picture altering procedure utilized because of its straightforwardness and adequacy, in which parts of the first picture is replicated, moved to a coveted area and glued. On the off chance that the altered pictures are mishandled, it might prompt potential social, legitimate or private outcomes. To this end, it's exceptionally essential and furthermore difficult to discover compelling strategies to distinguish computerized picture fabrications. In this Work, a quick keypoint based technique to identify picture duplicate move falsification is proposed in light of the SURF (Speed up Robust Features) descriptors, which are invariant to pivot, scaling and so forth. Consequences of trials show that the proposed strategy is substantial in distinguishing the picture area duplication and very vigorous to added substance clamor and obscuring. [ABSTRACT FROM AUTHOR]

Nowadays, determining if an image appeared somewhere on the web or in a magazine or is authentic or not has become crucial. Image forensics methods based on features have demonstrated so far to be very effective in detecting forgeries in which a portion of an image is cloned somewhere else onto the same image. Anyway such techniques cannot be adopted to deal with splicing attack, that is, when the image portion comes from another picture that then, usually, is not available anymore for an operation of feature match. In this paper, a procedure in which these techniques could also be employed will be shown to get rid of splicing attack by resorting to the use of some repositories of images available on the Internet like Google Images or TinEye Reverse Image Search. Experimental results are presented on some real case images retrieved on the Internet to demonstrate the capacity of the proposed procedure. [ABSTRACT FROM AUTHOR]

Only one study to date has explored the potential of innominate measurements for the estimation of stature in forensic contexts. The acetabulum, however, is among the skeletal regions most resistant to the processes of destruction and often survives intact. In this investigation, diameter, area and perimeter dimensions of the acetabulum were recorded from digital photographs in a documented French sample, consisting of 36 males and 33 females. Linear regression equations for estimating stature were then generated for each dimension in both males and females. Correlation coefficients for the models ranged fromr = 0.401 tor = 0.474, and standard error of the estimate (SEE) values were between 5.7 cm and 6.1 cm. The highest correlation coefficients in both sexes were observed for the diameter of the acetabulum. These results indicate that acetabular dimensions should only be used for stature estimation when more accurate bones are unavailable for analysis. [ABSTRACT FROM AUTHOR]

This work presents a new approach toward copy-move forgery detection based on multi-scale analysis and voting processes of a digital image. Given a suspicious image, we extract interest points robust to scale and rotation finding possible correspondences among them. We cluster correspondent points into regions based on geometric constraints. Thereafter, we construct a multi-scale image representation and for each scale, we examine the generated groups using a descriptor strongly robust to rotation, scaling and partially robust to compression, which decreases the search space of duplicated regions and yields a detection map. The final decision is based on a voting process among all detection maps. We validate the method using various datasets comprising original and realistic image clonings. We compare the proposed method to 15 others from the literature and report promising results. [ABSTRACT FROM AUTHOR]

This research investigates the forensics and anti-forensics of video editing operations, including shot insertion and deletion, which are common practices of content tampering. The proposed methodology is based on the fact that excessive prediction residuals in the H.264/AVC encoding may appear in the inter-coded frames that were intra-coded. For the anti-forensics, the relationships of coding modes in consecutive frames are employed to determine whether the intra-prediction can be applied during the re-encoding process of the tampered video. The correspondences of coding parameters and the bit-rates are then examined to predict the targeted distribution of quantization indices. Some nonzero indices are modified for satisfying the targeted distribution and generating a video that does not seem an edited one. Next, two forensic methods are presented. The first method evaluates the energy of resulting residuals after the deblocking filtering to reveal the editing operations that have been applied. The second method makes use of the rate control mechanism to check the quantization parameters. Although there exist certain limitations, the methods deserve more discussions under appropriate conditions. [ABSTRACT FROM AUTHOR]

We describe a method for detecting physical inconsistencies in lighting from the shading and shadows in an image. This method imposes a multitude of shading- and shadow-based constraints on the projected location of a distant point light source. The consistency of a collection of such constraints is posed as a linear programming problem. A feasible solution indicates that the combination of shading and shadows is physically consistent, while a failure to find a solution provides evidence of photo tampering. [ABSTRACT FROM AUTHOR]

The article discusses a geometric algorithm developed by researchers such as Hany Farid at the Image Science Group at Dartmouth College to analyze the lighting and shadows of photographs in order to determine their authenticity, known as digital forensics.

The article reports that president Ryoji Noyori of the Japanese research institute RIKEN has directed all laboratory and research group leaders to check all of their previous publications for doctored images and plagiarism after the discovery of fraud in 2014 papers appearing in "Nature" magazine.