0000000000516052
AUTHOR
Alessandro Piva
A vision-based fully automated approach to robust image cropping detection
Abstract The definition of valid and robust methodologies for assessing the authenticity of digital information is nowadays critical to contrast social manipulation through the media. A key research topic in multimedia forensics is the development of methods for detecting tampered content in large image collections without any human intervention. This paper introduces AMARCORD (Automatic Manhattan-scene AsymmetRically CrOpped imageRy Detector), a fully automated detector for exposing evidences of asymmetrical image cropping on Manhattan-World scenes. The proposed solution estimates and exploits the camera principal point, i.e., a physical feature extracted directly from the image content th…
Experiencing with electronic image stabilization and PRNU through scene content image registration
Abstract This paper explores content-based image registration as a means of dealing with and understanding better Electronic Image Stabilization (EIS) in the context of Photo Response Non-Uniformity (PRNU) alignment. A novel and robust solution to extrapolate the transformation relating the different image output formats for a given device model is proposed. This general approach can be adapted to specifically extract the scale factor (and, when appropriate, the translation) so as to align native resolution images to video frames, with or without EIS on, and proceed to compare PRNU patterns. Comparative evaluations show that the proposed approach outperforms those based on brute-force and p…
Prnu Pattern Alignment for Images and Videos Based on Scene Content
This paper proposes a novel approach for registering the PRNU pattern between different camera acquisition modes by relying on the imaged scene content. First, images are aligned by establishing correspondences between local descriptors: The result can then optionally be refined by maximizing the PRNU correlation. Comparative evaluations show that this approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. The proposed scene-based approach for PRNU pattern alignment is suitable for video source identification in multimedia forensics applications.
FISH: Face Intensity-Shape Histogram representation for automatic face splicing detection
Abstract Tampered images spread nowadays over any visual media influencing our judgement in many aspects of our life. This is particularly critical for face splicing manipulations, where recognizable identities are put out of context. To contrast these activities on a large scale, automatic detectors are required. In this paper, we present a novel method for automatic face splicing detection, based on computer vision, that exploits inconsistencies in the lighting environment estimated from different faces in the scene. Differently from previous approaches, we do not rely on an ideal mathematical model of the lighting environment. Instead, our solution, built upon the concept of histogram-ba…