The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability have unfortunately made SAR data susceptible to malicious alterations, such as local editing applied to the images for inserting or covering the presence of sensitive targets. To contrast malicious manipulations, in the last years the forensic community has begun to dig into the SAR manipulation issue, proposing detectors that effectively localize the tampering traces in amplitude images. Nonetheless, in this paper we demonstrate that an expert practitioner can exploit the complex nature of SAR data to obscure any signs of manipulation within a locally altered amplitude image. We refer to this approach as a counter-forensic attack. To achieve the concealment of manipulation traces, the attacker can simulate a re-acquisition of the manipulated scene by the SAR system that initially generated the pristine image. In doing so, the attacker can obscure any evidence of manipulation, making it appear as if the image was legitimately produced by the system. This attack has unique features that make it both highly generalizable and relatively easy to apply. First, it is a black-box attack, meaning it is not designed to deceive a specific forensic detector. Furthermore, it does not require a training phase and is not based on adversarial operations. We assess the effectiveness of the proposed counter-forensic approach across diverse scenarios, examining various manipulation operations. The obtained results indicate that our devised attack successfully eliminates traces of manipulation, deceiving even the most advanced forensic detectors.

Satellite imagery has a vital role in many applications and several techniques exist to enhance its quality. An example is given by image Super Resolution (SR), which aims at increasing the pixel resolution to recover lost high-frequency details. Due to their importance, satellite images are also a target for malicious manipulations. In such a context, knowing if an image has been super-resolved (SRV) is crucial for guaranteeing the correct usage of this kind of data. In this paper, we propose a pipeline for detecting satellite images SRV through State-Of-The-Art (SOTA) techniques based on Convolutional Neural Networks. Our solution employs an anomaly detection algorithm, i.e., a one-class Support Vector Machine trained on Fourier spectral features of native high-resolution images. Even though we never process SRV samples during training, the experiments show that we can reject images generated through different SOTA techniques, and our solution is robust against image compression.

Overhead images can be obtained using different acquisition and processing techniques, and they are becoming more and more popular. As with common photographs, they can be forged and manipulated by malicious users. However, not all image forensics methods tailored to normal photos can be successfully applied out of the box to overhead images. In this paper we consider the problem of localizing copy-paste forgeries on panchromatic images acquired with different satellites. We leverage a set of Convolutional Neural Networks (CNNs) that extract traces of the acquisition satellite directly from image patches. We then determine whether an image region appears to have been acquired with a different satellite than the rest of the picture. Results show that the proposed technique outperforms more sophisticated image forensics tools tailoring common photographs.

In the last few years, several companies started offering the possibility of buying different kinds of overhead images acquired by satellites orbiting around the planet. This market is interesting for several customers, from those who simply fancy a shot of their house from space, to those aiming to acquire strategic information on portions of land. Due to the sensitive nature of this data, which can be maliciously altered by anyone, the forensic community has started investigating methodologies to verify overhead imagery authenticity and integrity. Within this context, in this paper we investigate the possibility of using Convolutional Neural Networks (CNNs) to attribute a panchromatic satellite image to the satellite used to acquire it. In our investigation we tackle both closed-set and, adapting Deep Ensemble (DE) and Monte Carlo Dropout (MCD) techniques, open-set image attribution problems.

In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in video sequences with incredibly realistic results and a very little effort. Despite the usefulness of these tools in many fields, if used maliciously, they can have a significantly bad impact on society (e.g., fake news spreading, cyber bullying through fake revenge porn). The ability of objectively detecting whether a face has been manipulated in a video sequence is then a task of utmost importance. In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques. In particular, we study the ensembling of different trained Convolutional Neural Network (CNN) models. In the proposed solution, different models are obtained starting from a base network (i.e., EfficientNetB4) making use of two different concepts: (i) attention layers; (ii) siamese training. We show that combining these networks leads to promising face manipulation detection results on two publicly available datasets with more than 119000 videos.