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Found 15 papers, 3 papers with code
DeepFeatureX Net: Deep Features eXtractors based Network for discriminating synthetic from real images
The scientific community is working to develop approaches that can discriminate the origin of digital images (real or AI-generated).
On the Exploitation of DCT-Traces in the Generative-AI Domain
Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions.
MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks
The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious uses, especially in sensitive areas like medical imaging.
A Novel Dataset for Non-Destructive Inspection of Handwritten Documents
Preliminary results on the proposed datasets show that 90% classification accuracy can be achieved on the first subset (documents written on both paper and pen and later digitized and on tablets) and 96% on the second portion of the data.
AI Mirage: The Impostor Bias and the Deepfake Detection Challenge in the Era of Artificial Illusions
This paper provides a comprehensive analysis of cognitive biases in forensics and digital forensics, examining their implications for decision-making processes in these fields.
Innovative Methods for Non-Destructive Inspection of Handwritten Documents
Handwritten document analysis is an area of forensic science, with the goal of establishing authorship of documents through examination of inherent characteristics.
Improving Video Deepfake Detection: A DCT-Based Approach with Patch-Level Analysis
A new algorithm for the detection of deepfakes in digital videos is presented.
Not with my name! Inferring artists' names of input strings employed by Diffusion Models
In this paper, a preliminary study to infer the probability of use of an artist's name in the input string of a generated image is presented.
Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models
The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task.
On the Exploitation of Deepfake Model Recognition
Despite recent advances in Generative Adversarial Networks (GANs), with special focus to the Deepfake phenomenon there is no a clear understanding neither in terms of explainability nor of recognition of the involved models.
Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images
Most recent style-transfer techniques based on generative architectures are able to obtain synthetic multimedia contents, or commonly called deepfakes, with almost no artifacts.
Fighting deepfakes by detecting GAN DCT anomalies
To properly contrast the Deepfake phenomenon the need to design new Deepfake detection algorithms arises; the misuse of this formidable A. I.
Fighting Deepfake by Exposing the Convolutional Traces on Images
In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed.
Preliminary Forensics Analysis of DeepFake Images
One of the most terrifying phenomenon nowadays is the DeepFake: the possibility to automatically replace a person's face in images and videos by exploiting algorithms based on deep learning.
DeepFake Detection by Analyzing Convolutional Traces
The Deepfake phenomenon has become very popular nowadays thanks to the possibility to create incredibly realistic images using deep learning tools, based mainly on ad-hoc Generative Adversarial Networks (GAN).
