4 research outputs found
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision
Convolutional Neural Networks (CNNs) have proved very accurate in multiple
computer vision image classification tasks that required visual inspection in
the past (e.g., object recognition, face detection, etc.). Motivated by these
astonishing results, researchers have also started using CNNs to cope with
image forensic problems (e.g., camera model identification, tampering
detection, etc.). However, in computer vision, image classification methods
typically rely on visual cues easily detectable by human eyes. Conversely,
forensic solutions rely on almost invisible traces that are often very subtle
and lie in the fine details of the image under analysis. For this reason,
training a CNN to solve a forensic task requires some special care, as common
processing operations (e.g., resampling, compression, etc.) can strongly hinder
forensic traces. In this work, we focus on the effect that JPEG has on CNN
training considering different computer vision and forensic image
classification problems. Specifically, we consider the issues that rise from
JPEG compression and misalignment of the JPEG grid. We show that it is
necessary to consider these effects when generating a training dataset in order
to properly train a forensic detector not losing generalization capability,
whereas it is almost possible to ignore these effects for computer vision
tasks
MULTIPLE JPEG COMPRESSION DETECTION THROUGH TASK-DRIVEN NON-NEGATIVE MATRIX FACTORIZATION
Due to the increasingly unbridled practice of sharing visual content on the web, tracing back past history of uploaded images is getting far from being an easy task. Nonetheless, forensic analysts might be interested in probing digital history of content published on the web to assess its authenticity. In this vein, a possible indicator of image integrity is the number of JPEG compressions a picture underwent. As a matter of fact, JPEG compression is typically operated first at image inception time directly on the acquisition device. Then, it is customary re-applied every time an image is manipulated or shared through social media. For this reason, the more the applied JPEG compressions, the more the likelihood that an image underwent some editing. In this work, we propose an algorithm to detect multiple JPEG compressions, specifically up to four coding cycles. This approach leverages the Task-driven Non-negative Matrix Factorization (TNMF) model, fed with histograms of the Discrete Cosine Transform (DCT) of the image under analysis. Experimental results show the effectiveness of the method if compared with the state-of-the-art, confirming this strategy as a viable solution for detecting multiple JPEG compressions
An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Images
Due to the reduction of technological costs and the increase of satellites
launches, satellite images are becoming more popular and easier to obtain.
Besides serving benevolent purposes, satellite data can also be used for
malicious reasons such as misinformation. As a matter of fact, satellite images
can be easily manipulated relying on general image editing tools. Moreover,
with the surge of Deep Neural Networks (DNNs) that can generate realistic
synthetic imagery belonging to various domains, additional threats related to
the diffusion of synthetically generated satellite images are emerging. In this
paper, we review the State of the Art (SOTA) on the generation and manipulation
of satellite images. In particular, we focus on both the generation of
synthetic satellite imagery from scratch, and the semantic manipulation of
satellite images by means of image-transfer technologies, including the
transformation of images obtained from one type of sensor to another one. We
also describe forensic detection techniques that have been researched so far to
classify and detect synthetic image forgeries. While we focus mostly on
forensic techniques explicitly tailored to the detection of AI-generated
synthetic contents, we also review some methods designed for general splicing
detection, which can in principle also be used to spot AI manipulate imagesComment: 25 pages, 17 figures, 5 tables, APSIPA 202