Copy-move forgery detection using the segment gradient orientation histogram

The ready availability of image-editing software makes ensuring the authenticity of images an important issue. The most common type of image tampering is cloning, or Copy-Move Forgery (CMF), in which part(s) of the image are copied and pasted back into the same image. One possible transformation is where an object is copied, rotated and pasted; this type of forgery is called Copy-Rotate-Move Forgery (CRMF). Applying post-processing can be used to produce more realistic doctored images and thus can increase the difficulty of forgery detection. This paper presents a novel segmentation-based Copy-Move forgery detection method. A new method has been developed to segment the Copy-Move objects in a consistent way that is more efficient than Simple Linear Iterative Clustering (SLIC) segmentation for CMF/CRMF. We propose a new method to describe irregular shaped blocks (segments). The Segment Gradient Orientation Histogram (SGOH), is used to describe the gradient distribution of each segment. The quality of initial matches is improved by applying hysteresis to grow the primary detection regions. We show that the proposed method can effectively detect forgery involving translation and rotation. Moreover, the proposed method can detect forgery in images with blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise

A Forensic Scheme for Revealing Post-processed Region Duplication Forgery in Suspected Images

Recent researches have demonstrated that local interest points alone can be employed to detect region duplication forgery in image forensics. Authentic images may be abused by copy-move tool in Adobe Photoshop to fully contained duplicated regions such as objects with high primitives such as corners and edges. Corners and edges represent the internal structure of an object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be localised using scale-invariant features transform (SIFT) algorithm. In this paper, we provide an image forgery detection technique by using local interest points. Local interest points can be exposed by extracting adaptive non-maximal suppression (ANMS) keypoints from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS keypoints can be effectively utilised to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase quantisation (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v2 image datasets

Passive Techniques for Detecting and Locating Manipulations in Digital Images

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19-11-2020El numero de camaras digitales integradas en dispositivos moviles as como su uso en la vida cotidiana esta en continuo crecimiento. Diariamente gran cantidad de imagenes digitales, generadas o no por este tipo de dispositivos, circulan en Internet o son utilizadas como evidencias o pruebas en procesos judiciales. Como consecuencia, el analisis forense de imagenes digitales cobra importancia en multitud de situaciones de la vida real. El analisis forense de imagenes digitales se divide en dos grandes ramas: autenticidad de imagenes digitales e identificacion de la fuente de adquisicion de una imagen. La primera trata de discernir si una imagen ha sufrido algun procesamiento posterior al de su creacion, es decir, que no haya sido manipulada. La segunda pretende identificar el dispositivo que genero la imagen digital. La verificacion de la autenticidad de imagenes digitales se puedellevar a cabo mediante tecnicas activas y tecnicas pasivas de analisis forense. Las tecnicas activas se fundamentan en que las imagenes digitales cuentan con \marcas" presentes desde su creacion, de forma que cualquier tipo de alteracion que se realice con posterioridad a su generacion, modificara las mismas, y, por tanto, permitiran detectar si ha existido un posible post-proceso o manipulacion...The number of digital cameras integrated into mobile devices as well as their use in everyday life is continuously growing. Every day a large number of digital images, whether generated by this type of device or not, circulate on the Internet or are used as evidence in legal proceedings. Consequently, the forensic analysis of digital images becomes important in many real-life situations. Forensic analysis of digital images is divided into two main branches: authenticity of digital images and identi cation of the source of acquisition of an image. The first attempts to discern whether an image has undergone any processing subsequent to its creation, i.e. that it has not been manipulated. The second aims to identify the device that generated the digital image. Verification of the authenticity of digital images can be carried out using both active and passive forensic analysis techniques. The active techniques are based on the fact that the digital images have "marks"present since their creation so that any type of alteration made after their generation will modify them, and therefore will allow detection if there has been any possible post-processing or manipulation. On the other hand, passive techniques perform the analysis of authenticity by extracting characteristics from the image...Fac. de InformáticaTRUEunpu

Copy-move forgery detection using convolutional neural network and K-mean clustering

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms

Detection of incorrect and inappropriateImagefrom Tweets in Social Network

Digital imaging has grown to become the prevalent technology for creating, processing, and storing digital memory and proof. Though this technology brings many leverage, it can be used as a ambiguous tool for covering details and evidences. This is because today digital images can be tampered in such supremacy that forgery cannot be find visually. In fact, the immunity concern of digital content has arisen a long time ago and different methods to verify the efficiency of digital images have been developed. Digital images offer many features for forgery detection algorithm to take precedence of specifically the color and brightness of individual pixels as well as an image�s resolution and format. These properties grant for analysis and similarity between the significance of digital forgeries in an attempt to develop an algorithm for detecting image tampering. This paper presents a technique for image copy or move image forgery detection using Radix Sort, FasterK-means clustering algorithm & DCT

Counter-forensics of SIFT-based copy-move detection by means of keypoint classification

Copy-move forgeries are very common image manipulations that are often carried out with malicious intents. Among the techniques devised by the 'Image Forensic' community, those relying on scale invariant feature transform (SIFT) features are the most effective ones. In this paper, we approach the copy-move scenario from the perspective of an attacker whose goal is to remove such features. The attacks conceived so far against SIFT-based forensic techniques implicitly assume that all SIFT keypoints have similar properties. On the contrary, we base our attacking strategy on the observation that it is possible to classify them in different typologies. Also, one may devise attacks tailored to each specific SIFT class, thus improving the performance in terms of removal rate and visual quality. To validate our ideas, we propose to use a SIFT classification scheme based on the gray scale histogram of the neighborhood of SIFT keypoints. Once the classification is performed, we then attack the different classes by means of class-specific methods. Our experiments lead to three interesting results: (1) there is a significant advantage in using SIFT classification, (2) the classification-based attack is robust against different SIFT implementations, and (3) we are able to impair a state-of-the-art SIFT-based copy-move detector in realistic cases

Removal and injection of keypoints for SIFT-based copy-move counter-forensics

Recent studies exposed the weaknesses of scale-invariant feature transform (SIFT)-based analysis by removing keypoints without significantly deteriorating the visual quality of the counterfeited image. As a consequence, an attacker can leverage on such weaknesses to impair or directly bypass with alarming efficacy some applications that rely on SIFT. In this paper, we further investigate this topic by addressing the dual problem of keypoint removal, i.e., the injection of fake SIFT keypoints in an image whose authentic keypoints have been previously deleted. Our interest stemmed from the consideration that an image with too few keypoints is per se a clue of counterfeit, which can be used by the forensic analyst to reveal the removal attack. Therefore, we analyse five injection tools reducing the perceptibility of keypoint removal and compare them experimentally. The results are encouraging and show that injection is feasible without causing a successive detection at SIFT matching level. To demonstrate the practical effectiveness of our procedure, we apply the best performing tool to create a forensically undetectable copy-move forgery, whereby traces of keypoint removal are hidden by means of keypoint injection