Preliminary Forensics Analysis of DeepFake Images

One of the most terrifying phenomenon nowadays is the DeepFake: the possibility to automatically replace a person's face in images and videos by exploiting algorithms based on deep learning. This paper will present a brief overview of technologies able to produce DeepFake images of faces. A forensics analysis of those images with standard methods will be presented: not surprisingly state of the art techniques are not completely able to detect the fakeness. To solve this, a preliminary idea on how to fight DeepFake images of faces will be presented by analysing anomalies in the frequency domain.Comment: Accepted at IEEE AEIT International Annual Conference 202

First Quantization Coefficient Extraction from Double Compressed JPEG Images

Abstract. In the forensics domain can be useful to recover image history, and in particular whether or not it has been doubly compressed. Clarify this point allows to assess if, in addition to the compression at the time of shooting, the picture was decompressed and then resaved. This is not a clear indication of forgery, but it can justify further investigations. In this paper we propose a novel technique able to retrieve the coefficients of the first compression in a double compressed JPEG image when the second compression is lighter than the first one. The proposed approach exploits the effects of successive quantizations followed by dequantization to recover the original compression parameters. Experimental results and comparisons with a state of the art method confirm the effectiveness of the proposed approach