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

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

Autenticación de imágenes digitales mediante patrones locales de texturas

La autenticidad de una imagen digital sufre graves amenazas debido a la existencia de poderosas herramientas para la edición de imágenes digitales que facilitan la modificación del contenido de las mismas sin dejar huellas visibles de tales cambios. Este problema unido a la facilidad de distribución de la información a través de plataformas digitales como blogs, Internet o redes sociales, ha provocado que la sociedad tienda a aceptar como cierto todo lo que ve sin cuestionar su veracidad. En este trabajo se propone un método de autenticación de imágenes digitales mediante el análisis de patrones locales de textura. El sistema propuesto combina el patrón binario local con la transformada discreta wavelet y la transformada discreta del coseno para extraer las características de cada uno de los bloques de la imagen investigada. Posteriormente, se utiliza la máquina de soporte vectorial para crear el modelo que permita la verificación de la autenticidad de una imagen. Para la evaluación del método propuesto se realizaron experimentos con bases de datos públicas de imágenes falsificadas que son ampliamente utilizadas en la literatura

The contour tree image encoding technique and file format

The process of contourization is presented which converts a raster image into a discrete set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimises noticeable artifacts in the simplified image. The contour merging technique offers a complementary lossy compression system to the QDCT (Quantised Discrete Cosine Transform). The artifacts introduced by the two methods are very different; QDCT produces a general blurring and adds extra highlights in the form of overshoots, whereas contour merging sharpens edges, reduces highlights and introduces a degree of false contouring. A format based on the contourization technique which caters for most image types is defined, called the contour tree image format. Image operations directly on this compressed format have been studied which for certain manipulations can offer significant operational speed increases over using a standard raster image format. A couple of examples of operations specific to the contour tree format are presented showing some of the features of the new format.Science and Engineering Research Counci

The use of computer-aided drug design in small molecule drug discovery

Drug discovery is one of the most challenging research fields that contributes to the birth of novel drugs for therapeutic use. Due to the complexity and intricate nature of the research, lengthy processes are involved in identifying potential hit molecules for a therapeutic target. To shorten the time required to reach the hit-to-lead stage, computer-aided drug design (CADD) has been used to expedite the process and reduce laboratory expenses. Common strategies used within CADD involve structure-based drug design (SBDD) and ligand-based drug design (LBDD). Both strategies were used extensively in two projects showing the complementarity of each strategy throughout the process. In this work, two separate drug discovery projects are detailed: Design, synthesis and molecular docking study of novel tetrahydrocurcumin analogues as potential sarcoplasmic-endoplasmic reticulum calcium ATPases (SERCA) inhibitors – details the identification, synthesis and testing of potential hit candidate(s) targeting SERCA by using SBDD Filamenting temperature-sensitive mutant Z (FtsZ) as therapeutic target in ligand-based drug design – details the identification, synthesis and testing of potential hit molecule(s) targeting FtsZ In the first project, homology modelling and virtual compound library screening were utilised as the SBDD methods to identify potential hit molecules for testing in P-type calcium ATPases such as SERCA. Preliminary results have found compound 20, an analogue of tetrahydrocurcumin, to show some SERCA inhibitory effect at 300µM based on a SERCA-specific calcium signalling assay performed via fluorometric imaging plate reader. Molecular docking study has also reflected this outcome with desirable ligand-protein binding energies found for 20 when compared with other tested ligands. Pharmacophore screening was used as the main LBDD method in the second project to identify probable hit candidates targeting FtsZ. Potential ligands were synthesised, and tested for antibacterial effect in Bacillus Subtilis strain 168 (Bs168) and Streptococcus pneumoniae strain R6 (SpnR6) cells. One of the tetrahydrocurcumin analogues, compound 4, was found to have minimum inhibitory concentration (MIC) ≤ 10 µM in Bs168 cells and ≤ 2 µM in spnR6 cells. The IC50 values for 4 were 9.1 ± 0.01 µM and 1 ± 0.01 µM in Bs168 and SpnR6 cells respectively. The MIC of 4 was found to be very similar to the MIC of compound 1, a known hit compound targeting against Bs168 cells. On the other hand, the MIC of 4 was lower than the MIC (> 64 µg/mL) of a well-known FtsZ inhibitor, PC190723, against S. pneumoniae. Subsequent molecular docking analyses were completed to evaluate the ligand-protein binding energies to correlate against the testing results. Both compounds 20 and 4 possess some structural similarities and differences that may confer their different effects in these protein targets, which render both with potentials to become the next lead molecules for future development