Computer Graphic and Photographic Image Classification using Local Image Descriptors

With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy

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

Digital forensics trends and future

Nowadays, rapid evolution of computers and mobile phones has caused these devices to be used in criminal activities. Providing appropriate and sufficient security measures is a difficult job due to complexity of devices which makes investigating crimes involving these devices even harder. Digital forensic is the procedure of investigating computer crimes in the cyber world. Many researches have been done in this area to help forensic investigation to resolve existing challenges. This paper attempts to look into trends of applications of digital forensics and security at hand in various aspects and provide some estimations about future research trends in this area

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