Picture for Francesco Marra

Francesco Marra

Are GAN generated images easy to detect? A critical analysis of the state-of-the-art

Add code
Apr 06, 2021
Figure 1 for Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Figure 2 for Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Figure 3 for Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Figure 4 for Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
Viaarxiv icon

Combining PRNU and noiseprint for robust and efficient device source identification

Add code
Jan 17, 2020
Figure 1 for Combining PRNU and noiseprint for robust and efficient device source identification
Figure 2 for Combining PRNU and noiseprint for robust and efficient device source identification
Figure 3 for Combining PRNU and noiseprint for robust and efficient device source identification
Figure 4 for Combining PRNU and noiseprint for robust and efficient device source identification
Viaarxiv icon

Incremental learning for the detection and classification of GAN-generated images

Add code
Oct 06, 2019
Figure 1 for Incremental learning for the detection and classification of GAN-generated images
Figure 2 for Incremental learning for the detection and classification of GAN-generated images
Figure 3 for Incremental learning for the detection and classification of GAN-generated images
Figure 4 for Incremental learning for the detection and classification of GAN-generated images
Viaarxiv icon

A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection

Add code
Sep 15, 2019
Figure 1 for A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Figure 2 for A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Figure 3 for A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Figure 4 for A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection
Viaarxiv icon

Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers

Add code
Feb 20, 2019
Figure 1 for Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Figure 2 for Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Figure 3 for Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Figure 4 for Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Viaarxiv icon

Do GANs leave artificial fingerprints?

Add code
Dec 31, 2018
Figure 1 for Do GANs leave artificial fingerprints?
Figure 2 for Do GANs leave artificial fingerprints?
Figure 3 for Do GANs leave artificial fingerprints?
Figure 4 for Do GANs leave artificial fingerprints?
Viaarxiv icon

Analysis of adversarial attacks against CNN-based image forgery detectors

Add code
Aug 25, 2018
Figure 1 for Analysis of adversarial attacks against CNN-based image forgery detectors
Figure 2 for Analysis of adversarial attacks against CNN-based image forgery detectors
Figure 3 for Analysis of adversarial attacks against CNN-based image forgery detectors
Figure 4 for Analysis of adversarial attacks against CNN-based image forgery detectors
Viaarxiv icon