Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. The automated procedure consists of three primary steps. First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. We use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered with and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.Comment: arXiv admin note: text overlap with arXiv:1802.0315

Review on local binary patterns variants as texture descriptors for copy-move forgery detection

Past decades had seen the concerned by researchers in authenticating the originality of an image as the result of advancement in computer technology. Many methods have been developed to detect image forgeries such as copy-move, splicing, resampling and et cetera. The most common type of image forgery is copy-move where the copied region is pasted on the same image. The existence of high similarity in colour and textures of both copied and pasted images caused the detection of the tampered region to be very difficult. Additionally, the existence of post-processing methods makes it more challenging. In this paper, Local Binary Pattern (LBP) variants as texture descriptors for copy-move forgery detection have been reviewed. These methods are discussed in terms of introduction and methodology in copy-move forgery detection. These methods are also compared in the discussion section. Finally, their strengths and weaknesses are summarised, and some future research directions were pointed out

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

الكشف عن تزوير الصور الرقمية باستخدام منهجية متكيفة ومُحسَّنة

الدراسة المقدمة من خلال هذا البحث تهتم بموضوع الصور الرقمية، وهي موجهة بشكلٍ خاصٍ للكشف عن التزوير الذي يمكن أن يتم تطبيقه على الصور الرقمية. إن النظام المُقترح يقدم تطبيقاً ديناميكياً متكيفاً، يستطيع الكشف عن النوعين الأكثر انتشاراً واستخداماً من التزوير وهما: تزوير النسخ، وتزوير الدمج. حيث يمكن من خلال هذا النظام الكشف عن هذين النوعين من التزوير، وذلك في أنواع وأحجام مختلفة من الصور، على عكس العديد من الدراسات السابقة والتي كانت مخصصة لنوع تزوير معين أو لصورة بمقاييس وشروط محددة. يقوم التطبيق بشكل ديناميكي بالتكيف مع الصورة المُعطاة، واختيار الخوارزمية التي تلائم هذه الصورة، بحيث يتم التوصل إلى النتيجة الأفضل في الكشف عن التزوير وذلك بما يناسب معطيات الصورة وخصائصها. كما يقدّم النظام المقترح تحسيناً فيما يتعلق بعدد الإنذارات الخاطئة التي كانت تصدر عن الأنظمة الأساسية التي يعتمد عليها التطبيق في الكشف عن تزوير النسخ، حيث أن النظامين الأساسيين المُقدمين في دراسات سابقة، كانا يعانيان من عدد كبير من الإنذارات الخاطئة والتي كانت تُظهر وجود تزوير في حين أن الصورة أصلية غير مزورة. لذلك كان أحد أهداف هذه الدراسة هو البحث عن أسباب هذه الإنذارات الخاطئة في كل طريقة على حدة، ومعالجة هذه الأسباب بهدف تحسين أداء الخوارزميات الأصلية.

Detecting splicing and copy-move attacks in color images

Image sensors are generating limitless digital images every day. Image forgery like splicing and copy-move are very common type of attacks that are easy to execute using sophisticated photo editing tools. As a result, digital forensics has attracted much attention to identify such tampering on digital images. In this paper, a passive (blind) image tampering identification method based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) has been proposed. First, the chroma components of an image is divided into fixed sized non-overlapping blocks and 2D block DCT is applied to identify the changes due to forgery in local frequency distribution of the image. Then a texture descriptor, LBP is applied on the magnitude component of the 2D-DCT array to enhance the artifacts introduced by the tampering operation. The resulting LBP image is again divided into non-overlapping blocks. Finally, summations of corresponding inter-cell values of all the LBP blocks are computed and arranged as a feature vector. These features are fed into a Support Vector Machine (SVM) with Radial Basis Function (RBF) as kernel to distinguish forged images from authentic ones. The proposed method has been experimented extensively on three publicly available well-known image splicing and copy-move detection benchmark datasets of color images. Results demonstrate the superiority of the proposed method over recently proposed state-of-the-art approaches in terms of well accepted performance metrics such as accuracy, area under ROC curve and others.2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 201

Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications

Vers une analyse des rumeurs dans les réseaux sociaux basée sur la véracité des images : état de l'art

National audienceLe développement rapide des réseaux sociaux a favorisé l'échange d'une masse de données importante, mais aussi la propagation de fausses informations. De nombreux travaux se sont intéressés à la détection des rumeurs, basés principalement sur l'analyse du contenu textuel des messages. Cependant, le contenu visuel, notamment les images, demeure ignoré ou peu exploité. Or, les données visuelles sont très répandues sur les médias sociaux et leur exploitation s'avère être importante pour analyser les rumeurs. Dans cet article, nous présentons une synthèse de l'état de l'art des travaux relatifs à la classi?cation des rumeurs et résumons les tâches principales de ce processus, ainsi que les approches suivies pour analyser ce phénomène. Nous nous focalisons plus particulièrement sur les techniques adoptées pour véri?er la véracité des images. Nous discutons également les jeux de données utilisés pour l'analyse des rumeurs et présentons les pistes de recherche que nous comptons explorer.Le développement rapide des réseaux sociaux a favorisé l'échange d'une masse de données importante, mais aussi la propagation de fausses informations. De nombreux travaux se sont intéressés à la détection des rumeurs, basés principalement sur l'analyse du contenu textuel des messages. Cependant, le contenu visuel, notamment les images, demeure ignoré ou peu exploité. Or, les données visuelles sont très répandues sur les médias sociaux et leur exploitation s'avère être importante pour analyser les rumeurs. Dans cet article, nous présentons une synthèse de l'état de l'art des travaux relatifs à la classification des rumeurs et résumons les tâches principales de ce processus, ainsi que les approches suivies pour analyser ce phénomène. Nous nous focalisons plus particulièrement sur les techniques adoptées pour vérifier la véracité des images. Nous discutons également les jeux de données utilisés pour l'analyse des rumeurs et présentons les pistes de recherche que nous comptons explorer

Image splicing detection scheme using adaptive threshold mean ternary pattern descriptor

The rapid growth of image editing applications has an impact on image forgery cases. Image forgery is a big challenge in authentic image identification. Images can be readily altered using post-processing effects, such as blurring shallow depth, JPEG compression, homogenous regions, and noise to forge the image. Besides, the process can be applied in the spliced image to produce a composite image. Thus, there is a need to develop a scheme of image forgery detection for image splicing. In this research, suitable features of the descriptors for the detection of spliced forgery are defined. These features will reduce the impact of blurring shallow depth, homogenous area, and noise attacks to improve the accuracy. Therefore, a technique to detect forgery at the image level of the image splicing was designed and developed. At this level, the technique involves four important steps. Firstly, convert colour image to three colour channels followed by partition of image into overlapping block and each block is partitioned into non-overlapping cells. Next, Adaptive Thresholding Mean Ternary Pattern Descriptor (ATMTP) is applied on each cell to produce six ATMTP codes and finally, the tested image is classified. In the next part of the scheme, detected forgery object in the spliced image involves five major steps. Initially, similarity among every neighbouring district is computed and the two most comparable areas are assembled together to the point that the entire picture turns into a single area. Secondly, merge similar regions according to specific state, which satisfies the condition of fewer than four pixels between similar regions that lead to obtaining the desired regions to represent objects that exist in the spliced image. Thirdly, select random blocks from the edge of the binary image based on the binary mask. Fourthly, for each block, the Gabor Filter feature is extracted to assess the edges extracted of the segmented image. Finally, the Support Vector Machine (SVM) is used to classify the images. Evaluation of the scheme was experimented using three sets of standard datasets, namely, the Institute of Automation, Chinese Academy of Sciences (CASIA) version TIDE 1.0 and 2.0, and Columbia University. The results showed that, the ATMTP achieved higher accuracy of 98.95%, 99.03% and 99.17% respectively for each set of datasets. Therefore, the findings of this research has proven the significant contribution of the scheme in improving image forgery detection. It is recommended that the scheme be further improved in the future by considering geometrical perspective