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Found 7 papers, 4 papers with code
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.
Combining PRNU and noiseprint for robust and efficient device source identification
PRNU-based image processing is a key asset in digital multimedia forensics.
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.
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.
