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Found 10 papers, 3 papers with code
Towards Universal GAN Image Detection
The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools.
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 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.
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.
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.
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.
