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Found 44 papers, 18 papers with code
Detecting Multimedia Generated by Large AI Models: A Survey
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.
HeadCraft: Modeling High-Detail Shape Variations for Animated 3DMMs
The decomposition of the parametric model and high-quality vertex displacements allows us to animate the model and modify it semantically.
Raising the Bar of AI-generated Image Detection with CLIP
Aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images.
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing.
Information Forensics and Security: A quarter-century-long journey
Information Forensics and Security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable.
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.
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Detecting fake images is becoming a major goal of computer vision.
Learning Pairwise Interaction for Generalizable DeepFake Detection
We obtain 98. 48% BOSC accuracy on the FF++ dataset and 90. 87% BOSC accuracy on the CelebDF dataset suggesting a promising direction for generalization of DeepFake detection.
TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization
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.
Ranked #1 on
Image Manipulation Detection
on Columbia
Training Data Improvement for Image Forgery Detection using Comprint
Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation.
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).
How to Boost Face Recognition with StyleGAN?
We show that a simple approach based on fine-tuning pSp encoder for StyleGAN allows us to improve upon the state-of-the-art facial recognition and performs better compared to training on synthetic face identities.
Comprint: Image Forgery Detection and Localization using Compression Fingerprints
In an attempt to fight fake news, forgery detection and localization methods were designed.
Deepfake audio detection by speaker verification
Thanks to recent advances in deep learning, sophisticated generation tools exist, nowadays, that produce extremely realistic synthetic speech.
Audio-Visual Person-of-Interest DeepFake Detection
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.
Towards Universal GAN Image Detection
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools.
Forensic Analysis of Synthetically Generated Western Blot Images
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.
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.
ID-Reveal: Identity-aware DeepFake Video Detection
A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method.
Deep learning in the ultrasound evaluation of neonatal respiratory status
Lung ultrasound imaging is reaching growing interest from the scientific community.
CNN-based fast source device identification
Source identification is an important topic in image forensics, since it allows to trace back the origin of an image.
Media Forensics and DeepFakes: an overview
With the rapid progress of recent years, techniques that generate and manipulate multimedia content can now guarantee a very advanced level of realism.
Combining PRNU and noiseprint for robust and efficient device source identification
PRNU-based image processing is a key asset in digital multimedia forensics.
SpoC: Spoofing Camera Fingerprints
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.
Incremental learning for the detection and classification of GAN-generated images
Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones.
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.
FaceForensics++: Learning to Detect Manipulated Facial Images
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
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.
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
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.
Guided patch-wise nonlocal SAR despeckling
We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery.
Facing Device Attribution Problem for Stabilized Video Sequences
Specifically, we propose: (i) a strategy to extract the characteristic fingerprint of a device, starting from either a set of images or stabilized video sequences; (ii) a strategy to match a stabilized video sequence with a given fingerprint in order to solve the device attribution problem.
Multimedia
Camera-based Image Forgery Localization using Convolutional Neural Networks
Camera fingerprints are precious tools for a number of image forensics tasks.
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.
Noiseprint: a CNN-based camera model fingerprint
Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images.
FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces
Research on the detection of face manipulations has been seriously hampered by the lack of adequate datasets.
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.
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.
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 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.
Image forgery detection based on the fusion of machine learning and block-matching methods
Dense local descriptors and machine learning have been used with success in several applications, like classification of textures, steganalysis, and forgery detection.
A novel framework for image forgery localization
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.
