Texture based Image Splicing Forgery Recognition using a Passive Approach

With the growing usage of the internet in daily life along with the usage of dominant picture editing software tools in creating forged pictures effortlessly, make us lose trust in the authenticity of the images. For more than a decade, extensive research is going on in the Image forensic area that aims at restoring trustworthiness in images by bringing various tampering detection techniques. In the proposed method, identification of image splicing technique is introduced which depends on the picture texture analysis which characterizes the picture areas by the content of the texture. In this method, an image is characterized by the regions of their texture content. The experimental outcomes describe that the proposed method is effective to identify spliced picture forgery with an accuracy of 79.5%

Exposing image forgery by detecting traces of feather operation

Powerful digital image editing tools make it very easy to produce a perfect image forgery. The feather operation is necessary when tampering an image by copy–paste operation because it can help the boundary of pasted object to blend smoothly and unobtrusively with its surroundings. We propose a blind technique capable of detecting traces of feather operation to expose image forgeries. We model the feather operation, and the pixels of feather region will present similarity in their gradient phase angle and feather radius. An effectual scheme is designed to estimate each feather region pixel׳s gradient phase angle and feather radius, and the pixel׳s similarity to its neighbor pixels is defined and used to distinguish the feathered pixels from un-feathered pixels. The degree of image credibility is defined, and it is more acceptable to evaluate the reality of one image than just using a decision of YES or NO. Results of experiments on several forgeries demonstrate the effectiveness of the technique

SRU-NET: SOBEL RESIDUAL U-NET FOR IMAGE MANIPULATION DETECTION

Recently, most successful image manipulation detection methods have been based on convolutional neural networks (CNNs). Nevertheless, Existing CNN methods have limited abilities. CNN-based detection networks tend to extract signal features strongly related to content. However, image manipulation detection tends to extract weak signal features that are weakly related to content. To address this issue, We propose a novel Sobel residual neural network with adaptive central difference convolution, an extension of the classical U-Net architecture, for image manipulation detection. Adaptive central differential convolution can capture the essential attributes of an image by gathering intensity and gradient information. Sobel residual gradient block can capture forgery edge discriminative details. Extensive experimental results show that our method can significantly improve the accuracy of localising the forged region compared with the state-of-the-art methods

Image forgery detection using textural features and deep learning

La croissance exponentielle et les progrès de la technologie ont rendu très pratique le partage de données visuelles, d'images et de données vidéo par le biais d’une vaste prépondérance de platesformes disponibles. Avec le développement rapide des technologies Internet et multimédia, l’efficacité de la gestion et du stockage, la rapidité de transmission et de partage, l'analyse en temps réel et le traitement des ressources multimédias numériques sont progressivement devenus un élément indispensable du travail et de la vie de nombreuses personnes. Sans aucun doute, une telle croissance technologique a rendu le forgeage de données visuelles relativement facile et réaliste sans laisser de traces évidentes. L'abus de ces données falsifiées peut tromper le public et répandre la désinformation parmi les masses. Compte tenu des faits mentionnés ci-dessus, la criminalistique des images doit être utilisée pour authentifier et maintenir l'intégrité des données visuelles. Pour cela, nous proposons une technique de détection passive de falsification d'images basée sur les incohérences de texture et de bruit introduites dans une image du fait de l'opération de falsification. De plus, le réseau de détection de falsification d'images (IFD-Net) proposé utilise une architecture basée sur un réseau de neurones à convolution (CNN) pour classer les images comme falsifiées ou vierges. Les motifs résiduels de texture et de bruit sont extraits des images à l'aide du motif binaire local (LBP) et du modèle Noiseprint. Les images classées comme forgées sont ensuite utilisées pour mener des expériences afin d'analyser les difficultés de localisation des pièces forgées dans ces images à l'aide de différents modèles de segmentation d'apprentissage en profondeur. Les résultats expérimentaux montrent que l'IFD-Net fonctionne comme les autres méthodes de détection de falsification d'images sur l'ensemble de données CASIA v2.0. Les résultats discutent également des raisons des difficultés de segmentation des régions forgées dans les images du jeu de données CASIA v2.0.The exponential growth and advancement of technology have made it quite convenient for people to share visual data, imagery, and video data through a vast preponderance of available platforms. With the rapid development of Internet and multimedia technologies, performing efficient storage and management, fast transmission and sharing, real-time analysis, and processing of digital media resources has gradually become an indispensable part of many people’s work and life. Undoubtedly such technological growth has made forging visual data relatively easy and realistic without leaving any obvious visual clues. Abuse of such tampered data can deceive the public and spread misinformation amongst the masses. Considering the facts mentioned above, image forensics must be used to authenticate and maintain the integrity of visual data. For this purpose, we propose a passive image forgery detection technique based on textural and noise inconsistencies introduced in an image because of the tampering operation. Moreover, the proposed Image Forgery Detection Network (IFD-Net) uses a Convolution Neural Network (CNN) based architecture to classify the images as forged or pristine. The textural and noise residual patterns are extracted from the images using Local Binary Pattern (LBP) and the Noiseprint model. The images classified as forged are then utilized to conduct experiments to analyze the difficulties in localizing the forged parts in these images using different deep learning segmentation models. Experimental results show that both the IFD-Net perform like other image forgery detection methods on the CASIA v2.0 dataset. The results also discuss the reasons behind the difficulties in segmenting the forged regions in the images of the CASIA v2.0 dataset

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector

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

Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena