Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy-clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts like JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating encoder and LSTM network. Finally, decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With predicted mask provided by final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets

Mobile-based Telemedicine Application using SVD and F-XoR Watermarking for Medical Images

منصة الخدمات الطبية عبارة عن تطبيق متنقل يتم من خلاله تزويد المرضى بتشخيصات الأطباء بناءً على المعلومات المستقاة من الصور الطبية. يجب ألا يتم تبديل محتوى هذه النتائج التشخيصية بشكل غير قانوني أثناء النقل ويجب إعادته إلى المريض الصحيح. في هذه المقالة، نقدم حلاً لهذه المشكلات باستخدام علامة مائية عمياء وقابلة للانعكاس وهشة استنادًا إلى مصادقة صورة المضيف. في الخوارزمية المقترحة، يتم استخدام الإصدار الثنائي من ترميز بوس_شوهوري _هوكوينجهام (BCH) للتقرير الطبي للمريض (PMR) والصورة الطبية الثنائية للمريض (PMI) بعد استخدام الغامض الحصري أو (F-XoR) لإنتاج العلامة الفريدة للمريض باستخدام مخطط المشاركة السرية (SSS). يتم استخدامه لاحقًا كعلامة مائية ليتم تضمينها في مضيف (PMI) باستخدام خوارزمية تحليل القيمة المفرد (SVD) العمياء القائمة على العلامة المائية. وهو حل جديد اقترحناه أيضًا بتطبيق SVD على صورة العلامة المائية العمياء. تحافظ الخوارزمية الخاصة بنا على مصادقة محتوى (PMI) أثناء النقل وملكية (PMR) للمريض لنقل التشخيص المصاحب فيما بعد إلى المريض الصحيح عبر تطبيق التطبيب عن بعد المحمول. يستخدم تقييم الخوارزمية لدينا علامات مائية مسترجعة توضح النتائج الواعدة لمقاييس الأداء العالية مقارنتا مع نتائج الاعمال السابقة في مقاييس الكشف عن التزوير وإمكانية الاسترداد الذاتي، مع قيمة 30NB PSNR، قيمة NC هي 0.99.A medical- service platform is a mobile application through which patients are provided with doctor’s diagnoses based on information gleaned from medical images. The content of these diagnostic results must not be illegitimately altered during transmission and must be returned to the correct patient. In this paper, we present a solution to these problems using blind, reversible, and fragile watermarking based on authentication of the host image. In our proposed algorithm, the binary version of the Bose_Chaudhuri_Hocquengham (BCH) code for patient medical report (PMR) and binary patient medical image (PMI) after fuzzy exclusive or (F-XoR) are used to produce the patient's unique mark using secret sharing schema (SSS). The patient’s unique mark is used later as a watermark to be embedded into host PMI using blind watermarking-based singular value decomposition (SVD) algorithm. This is a new solution that we also proposed to applying SVD into a blind watermarking image. Our algorithm preserves PMI content authentication during the transmission and PMR ownership to the patient for subsequently transmitting associated diagnosis to the correct patient via a mobile telemedicine application. The performance of experimental results is high compare to previous results, uses recovered watermarks demonstrating promising results in the tamper detection metrics and self-recovery capability, with 30db PSNR, NC value is 0.99

Validation and Data Repairing of Document Image using Steganography Method

This paper attempts to propose a novel technique of blind authentication based on the method of secret in addition to data repair capability for grayscale document images through the use of the Portable Network Graphics (PNG) image. For every block of a grayscale document image, an authentication signal is generated, which, along with the block content in binary, is transformed into numerous shares using the Shamir secret sharing scheme. The parameters involved are carefully selected so that as many shares as possible can be generated and embedded into an alpha channel plane. After this, the alpha channel plane is combined with the original grayscale image to yield a PNG image. During this process, the computed share values are recorded as a range of alpha channel values near their maximum value of 255 to return a transparent stego-image with a disguised effect. In the image authentication process, marking of an image block is done as tampered, if the authentication signal computed from the current block content does not match the one extracted from the shares embedded in the alpha channel plane. Each tampered block is then subjected to data repairing by a reverse Shamir scheme after collecting two shares from unmarked blocks. Procedures to protect the safety of the data that lies concealed in the alpha channel have been proposed. Decent experimental results demonstrate the efficiency of the proposed method

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

Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks

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., 64x64), 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.Comment: Submitted to Journal of Visual Communication and Image Representation (first submission: March 20, 2017; second submission: August 2, 2017

Secure and Robust Fragile Watermarking Scheme for Medical Images

Over the past decade advances in computer-based communication and health services, the need for image security becomes urgent to address the requirements of both safety and non-safety in medical applications. This paper proposes a new fragile watermarking based scheme for image authentication and self-recovery for medical applications. The proposed scheme locates image tampering as well as recovers the original image. A host image is broken into 4×4 blocks and Singular Value Decomposition (SVD) is applied by inserting the traces of block wise SVD into the Least Significant Bit (LSB) of the image pixels to figure out the transformation in the original image. Two authentication bits namely block authentication and self-recovery bits were used to survive the vector quantization attack. The insertion of self-recovery bits is determined with Arnold transformation, which recovers the original image even after a high tampering rate. SVD-based watermarking information improves the image authentication and provides a way to detect different attacked area. The proposed scheme is tested against different types of attacks such are text removal attack, text insertion attack, and copy and paste attack

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future