6 accepted papers
Attributing synthetic images to their source generative models is critical for digital forensics and security. While most existing attribution methods can distinguish images produced by known models and reject those from unknown ones, they are unable to verify whether a given image was produced by a
Determining the origin of a digital image or video, namely device source identification, is widely used in courtroom evidence and copyright protection. Currently, device source identification primarily focuses on images captured using single camera with default settings. However, with the advancemen…
Over the past decade, the proliferation of social networks introduced new challenges in the multimedia forensic field, such as the identification of the originating platform. Significant strides have been made in the characterization of digital images, exploiting features related to the media contai…
Detecting the origin of a digital video within a social network is a critical task that aids law enforcement and intelligence agencies in identifying the creators of misleading visual content. In this research, we introduce an innovative method for identifying the original social network of a video,…
The detection of images that are spliced from multiple sources is one important goal of image forensics. Several methods have been proposed for this task, but particularly since the rise of social media, it is an ongoing challenge to devise forensic approaches that are highly robust to common proces…
The Shift Aggregate Extract Network SAEN is an architecture for learning representations on social network data. SAEN decomposes input graphs into hierarchies made of multiple strata of objects. Vector representations of each object are learnt by applying 'shift', 'aggregate' and 'extract' operation…