Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces

Recent advances in deep learning have enabled forensics researchers to develop a new class of image splicing detection and localization algorithms. These algorithms identify spliced content by detecting localized inconsistencies in forensic traces using Siamese neural networks, either explicitly during analysis or implicitly during training. At the same time, deep learning has enabled new forms of anti-forensic attacks, such as adversarial examples and generative adversarial network (GAN) based attacks. Thus far, however, no anti-forensic attack has been demonstrated against image splicing detection and localization algorithms. In this paper, we propose a new GAN-based anti-forensic attack that is able to fool state-of-the-art splicing detection and localization algorithms such as EXIF-Net, Noiseprint, and Forensic Similarity Graphs. This attack operates by adversarially training an anti-forensic generator against a set of Siamese neural networks so that it is able to create synthetic forensic traces. Under analysis, these synthetic traces appear authentic and are self-consistent throughout an image. Through a series of experiments, we demonstrate that our attack is capable of fooling forensic splicing detection and localization algorithms without introducing visually detectable artifacts into an attacked image. Additionally, we demonstrate that our attack outperforms existing alternative attack approaches.

Recognition Model of Counterfeiting Digital Records of Biometric Photographic Image

Biometric portrait as one of the most important means of identifying requirements through strict definition of dimensional relationships, preservation of realistic information about all technical characteristics of the photographic image, so that all biometric values can be digitized and used in recognition. The great variety and accessibility of applications for digital processing of digital record of a photographic image has enabled a visually convincing display of a forged photograph that leaves a different impression on the viewer and transmits a different, that is, a forged message. Due to the need to prove the authenticity of the digital record of the photographic image, methods have been developed for the analysis of the record that can detect deviations from the real record even when there are no visual signs of processing the photographic image. Not all analysis techniques can detect certain methods of photo manipulation, so multiple digital photography detection and analysis techniques need to be applied. In order to prove its authenticity, the scientific paper deals with methods for analysis and detection of forgery of digital photography with respect to the digital record and the structure of JPEG format

EXIF as Language: Learning Cross-Modal Associations Between Images and Camera Metadata

We learn a visual representation that captures information about the camera that recorded a given photo. To do this, we train a multimodal embedding between image patches and the EXIF metadata that cameras automatically insert into image files. Our model represents this metadata by simply converting it to text and then processing it with a transformer. The features that we learn significantly outperform other self-supervised and supervised features on downstream image forensics and calibration tasks. In particular, we successfully localize spliced image regions "zero shot" by clustering the visual embeddings for all of the patches within an image.Comment: Project link: http://hellomuffin.github.io/exif-as-languag

Extracción y análisis de características para identificación, agrupamiento y modificación de la fuente de imágenes generadas por dispositivos móviles

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 02/10/2017.Nowadays, digital images play an important role in our society. The presence of mobile devices with integrated cameras is growing at an unrelenting pace, resulting in the majority of digital images coming from this kind of device. Technological development not only facilitates the generation of these images, but also the malicious manipulation of them. Therefore, it is of interest to have tools that allow the device that has generated a certain digital image to be identified. The digital image source can be identified through the features that the generating device permeates it with during the creation process. In recent years most research on techniques for identifying the source has focused solely on traditional cameras. The forensic analysis techniques of digital images generated by mobile devices are therefore of particular importance since they have specific characteristics which allow for better results, and forensic techniques for digital images generated by another kind of device are often not valid. This thesis provides various contributions in two of the main research lines of forensic analysis, the field of identification techniques and the counter-forensics or attacks on these techniques. In the field of digital image source acquisition identification techniques, both closed and open scenarios are addressed. In closed scenarios, the images whose acquisition source are to be determined belong to a group of devices known a priori. Meanwhile, an open scenario is one in which the images under analysis belong to a set of devices that is not known a priori by the fo rensic analyst. In this case, the objective is not t he concrete image acquisition source identification, but their classification into groups whose images all belong to the same mobile device. The image clustering t echniques are of particular interest in real situations since in many cases the forensic analyst does not know a priori which devices have generated certain images. Firstly, techniques for identifying the device type (computer, scanner or digital camera of the mobile device) or class (make and model) of the image acquisition source in mobile devices are proposed, which are two relevant branches of forensic analysis of mobile device images. An approach based on different types of image features and Support Vector Machine as a classifier is presented. Secondly, a technique for the ident ification in open scenarios that consists of grouping digital images of mobile devices according to the acquisition source is developed, that is to say, a class-grouping of all input images is performed. The proposal is based on the combination of hierarchical grouping and flat grouping using the Sensor Pattern Noise. Lastly, in the area of att acks on forensic t echniques, topics related to the robustness of the image source identificat ion forensic techniques are addressed. For this, two new algorithms based on the sensor noise and the wavelet transform are designed, one for the destruction of t he image identity and another for its fo rgery. Results obtained by the two algorithms were compared with other tools designed for the same purpose. It is worth mentioning that the solution presented in this work requires less amount and complexity of input data than the tools to which it was compared. Finally, these identification t echniques have been included in a tool for the forensic analysis of digital images of mobile devices called Theia. Among the different branches of forensic analysis, Theia focuses mainly on the trustworthy identification of make and model of the mobile camera that generated a given image. All proposed algorithms have been implemented and integrated in Theia thus strengthening its functionality.Actualmente las imágenes digitales desempeñan un papel importante en nuestra sociedad. La presencia de dispositivos móviles con cámaras fotográficas integradas crece a un ritmo imparable, provocando que la mayoría de las imágenes digitales procedan de este tipo de dispositivos. El desarrollo tecnológico no sólo facilita la generación de estas imágenes, sino también la manipulación malintencionada de éstas. Es de interés, por tanto, contar con herramientas que permitan identificar al dispositivo que ha generado una cierta imagen digital. La fuente de una imagen digital se puede identificar a través de los rasgos que el dispositivo que la genera impregna en ella durante su proceso de creación. La mayoría de las investigaciones realizadas en los últimos años sobre técnicas de identificación de la fuente se han enfocado únicamente en las cámaras tradicionales. Las técnicas de análisis forense de imágenes generadas por dispositivos móviles cobran, pues, especial importancia, ya que éstos presentan características específicas que permiten obtener mejores resultados, no siendo válidas muchas veces además las técnicas forenses para imágenes digitales generadas por otros tipos de dispositivos. La presente Tesis aporta diversas contribuciones en dos de las principales líneas del análisis forense: el campo de las t écnicas de identificación de la fuente de adquisición de imágenes digitales y las contramedidas o at aques a est as técnicas. En el primer campo se abordan tanto los escenarios cerrados como los abiertos. En el escenario denominado cerrado las imágenes cuya fuente de adquisición hay que determinar pertenecen a un grupo de dispositivos conocidos a priori. Por su parte, un escenario abierto es aquel en el que las imágenes pertenecen a un conjunto de dispositivos que no es conocido a priori por el analista forense. En este caso el obj etivo no es la identificación concreta de la fuente de adquisición de las imágenes, sino su clasificación en grupos cuyas imágenes pertenecen todas al mismo dispositivo móvil. Las técnicas de agrupamiento de imágenes son de gran interés en situaciones reales, ya que en muchos casos el analist a forense desconoce a priori cuáles son los dispositivos que generaron las imágenes. En primer lugar se presenta una técnica para la identificación en escenarios cerrados del tipo de dispositivo (computador, escáner o cámara digital de dispositivo móvil) o la marca y modelo de la fuente en dispositivos móviles, que son dos problemáticas relevantes del análisis forense de imágenes digitales. La propuesta muestra un enfoque basado en distintos tipos de características de la imagen y en una clasificación mediante máquinas de soporte vectorial. En segundo lugar se diseña una técnica para la identificación en escenarios abiertos que consiste en el agrupamiento de imágenes digitales de dispositivos móviles según la fuente de adquisición, es decir, se realiza un agrupamiento en clases de todas las imágenes de ent rada. La propuesta combina agrupamiento jerárquico y agrupamiento plano con el uso del patrón de ruido del sensor. Por último, en el área de los ataques a las técnicas fo renses se tratan temas relacionados con la robustez de las técnicas forenses de identificación de la fuente de adquisición de imágenes. Se especifican dos algoritmos basados en el ruido del sensor y en la transformada wavelet ; el primero destruye la identidad de una imagen y el segundo falsifica la misma. Los resultados obtenidos por estos dos algoritmos se comparan con otras herramientas diseñadas para el mismo fin, observándose que la solución aquí presentada requiere de menor cantidad y complejidad de datos de entrada. Finalmente, estas técnicas de identificación han sido incluidas en una herramienta para el análisis forense de imágenes digitales de dispositivos móviles llamada Theia. Entre las diferentes ramas del análisis forense, Theia se centra principalmente en la identificación confiable de la marca y el modelo de la cámara móvil que generó una imagen dada. Todos los algoritmos desarrollados han sido implementados e integrados en Theia, reforzando así su funcionalidad.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, firstly, we investigate how 13 most popular SNs treat the uploaded pictures, in order to identify a possible implementation of image watermarking techniques by respective SNs. Secondly, on these 13 SNs, we test the robustness of several image watermarking algorithms. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is robust enough in spite of the fact that the pictures get downgraded during the uploading process by SNs. The results of our analysis on a real dataset of 8,400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs.Comment: 43 pages, 6 figure