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Found 24 papers, 8 papers with code
Synthetic Image Verification in the Era of Generative AI: What Works and What Isn't There Yet
In this work we present an overview of approaches for the detection and attribution of synthetic images and highlight their strengths and weaknesses.
Raising the Bar of AI-generated Image Detection with CLIP
The aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images.
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulations
Here, we investigate this issue and propose M3Dsynth, a large dataset of manipulated Computed Tomography (CT) lung images.
A full-resolution training framework for Sentinel-2 image fusion
This work presents a new unsupervised framework for training deep learning models for super-resolution of Sentinel-2 images by fusion of its 10-m and 20-m bands.
Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity
In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images.
Ranked #1 on
Pansharpening
on GeoEye-1 PairMax
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Detecting fake images is becoming a major goal of computer vision.
On the detection of synthetic images generated by diffusion models
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).
Deepfake audio detection by speaker verification
Thanks to recent advances in deep learning, sophisticated generation tools exist, nowadays, that produce extremely realistic synthetic speech.
Towards Universal GAN Image Detection
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools.
Pansharpening by convolutional neural networks in the full resolution framework
A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images.
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
The advent of deep learning has brought a significant improvement in the quality of generated media.
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation.
Deep learning in the ultrasound evaluation of neonatal respiratory status
Lung ultrasound imaging is reaching growing interest from the scientific community.
Combining PRNU and noiseprint for robust and efficient device source identification
PRNU-based image processing is a key asset in digital multimedia forensics.
A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Due to limited computational and memory resources, current deep learning models accept only rather small images in input, calling for preliminary image resizing.
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Deep neural networks provide unprecedented performance in all image classification problems, taking advantage of huge amounts of data available for training.
Do GANs leave artificial fingerprints?
In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields.
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
Analysis of adversarial attacks against CNN-based image forgery detectors
With the ubiquitous diffusion of social networks, images are becoming a dominant and powerful communication channel.
Autoencoder with recurrent neural networks for video forgery detection
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.
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization.
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
We propose a new algorithm for the reliable detection and localization of video copy-move forgeries.
A reliable order-statistics-based approximate nearest neighbor search algorithm
We propose a new algorithm for fast approximate nearest neighbor search based on the properties of ordered vectors.
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes.
