Aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images.
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing.
Here, we investigate this issue and propose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lung images.
Detecting fake images is becoming a major goal of computer vision.
In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning.
Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks (GAN).
In an attempt to fight fake news, forgery detection and localization methods were designed.
Thanks to recent advances in deep learning, sophisticated generation tools exist, nowadays, that produce extremely realistic synthetic speech.
The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world.
no code implementations • 16 Dec 2021 • Sara Mandelli, Davide Cozzolino, Edoardo D. Cannas, Joao P. Cardenuto, Daniel Moreira, Paolo Bestagini, Walter J. Scheirer, Anderson Rocha, Luisa Verdoliva, Stefano Tubaro, Edward J. Delp
As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images.
The advent of deep learning has brought a significant improvement in the quality of generated media.
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.
Source identification is an important topic in image forensics, since it allows to trace back the origin of an image.
PRNU-based image processing is a key asset in digital multimedia forensics.
Given a GAN-generated image, we insert the traces of a specific camera model into it and deceive state-of-the-art detectors into believing the image was acquired by that model.
In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.
Ranked #1 on DeepFake Detection on FaceForensics
We devise a learning-based forensic detector which adapts well to new domains, i. e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training.
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets.
We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art.
Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos.
We propose a new algorithm for the reliable detection and localization of video copy-move forgeries.
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization.
We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors.
Dense local descriptors and machine learning have been used with success in several applications, like classification of textures, steganalysis, and forgery detection.
Image forgery localization is a very active and open research field for the difficulty to handle the large variety of manipulations a malicious user can perform by means of more and more sophisticated image editing tools.