With the rapid growth in deepfake video content, we re-quire improved and generalizable methods to detect them. Most existing detection methods either use uni-modal cues or rely on supervised training to capture the dissonance be-tween the audio and visual modalities. While the former disregards the audio-visual correspondences entirely, the lat-ter predominantly focuses on discerning audio-visual cues within the training corpus, thereby potentially overlooking correspondences that can help detect unseen deepfakes. We present Audio- Visual Feature Fusion (AVFF), a two-stage cross-modal learning method that explicitly captures the correspondence between the audio and visual modalities for improved deepfake detection. The first stage pursues representation learning via self-supervision on real videos to capture the intrinsic audio-visual correspondences. To extract rich cross-modal representations, we use contrastive learning and autoencoding objectives, and introduce a novel audio-visual complementary masking and feature fusion strategy. The learned representations are tuned in the second stage, where deepfake classification is pursued via super-vised learning on both real and fake videos. Extensive exper-iments and analysis suggest that our novel representation learning paradigm is highly discriminative in nature. We report 98.6% accuracy and 99.1% AUC on the FakeAVCeleb dataset, outperforming the current audio-visual state-of-the-art by 14.9% and 9.9%, respectively.

In the last few years, we have witnessed the rise of a series of deep learning methods to generate synthetic images that look extremely realistic. These techniques prove useful in the movie industry and for artistic purposes. However, they also prove dangerous if used to spread fake news or to generate fake online accounts. For this reason, detecting if an image is an actual photograph or has been synthetically generated is becoming an urgent necessity. This paper proposes a detector of synthetic images based on an ensemble of Convolutional Neural Networks (CNNs). We consider the problem of detecting images generated with techniques not available at training time. This is a common scenario, given that new image generators are published more and more frequently. To solve this issue, we leverage two main ideas: (i) CNNs should provide "orthogonal" results to better contribute to the ensemble; (ii) the original-image class is better defined than the synthetic-image one, thus it should be better trusted at testing time. Experiments show that pursuing these two ideas improves the detector accuracy on NVIDIA's newly generated StyleGAN3 images, never used in training.

The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose.

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.

In recent years, the number of images shared online has continuously grown. The forensics community has kept the pace by developing techniques to both reliably extract information from these images, but also to remove it. In particular, the latest developments in image anonymization methods exposes an attack vector when used by skilled ill-intentioned image producers that may want to elude prosecution. We present an approach to detect whether or not an image has undergone a laundering process, i.e., it has been tampered with so that its unique characterizing features have been changed to avoid detection. We focus on the photo response non uniformity (PRNU) noise unique to every imaging sensor, and we consider that an image has been "laundered" when we detect the absence of PRNU from an image. We propose a per image preprocessing pipeline that generates information-rich features later used as input of fine-tuned convolutional neural networks (CNNs). We study the performance of the proposed approach using various CNN architectures and blind anonymization techniques and show its effectiveness under several training and testing scenarios. Our results also show that CNN models trained with the proposed feature are capable of generalizing over unseen devices and are robust against non-geometric transformations.

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.