Detection of gan-generated fake images over social networks F Marra, D Gragnaniello, D Cozzolino, L Verdoliva 2018 IEEE conference on multimedia information processing and retrieval …, 2018 | 418 | 2018 |
Do gans leave artificial fingerprints? F Marra, D Gragnaniello, L Verdoliva, G Poggi 2019 IEEE conference on multimedia information processing and retrieval …, 2019 | 361 | 2019 |
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art D Gragnaniello, D Cozzolino, F Marra, G Poggi, L Verdoliva 2021 IEEE international conference on multimedia and expo (ICME), 1-6, 2021 | 167 | 2021 |
Incremental learning for the detection and classification of gan-generated images F Marra, C Saltori, G Boato, L Verdoliva 2019 IEEE international workshop on information forensics and security (WIFS …, 2019 | 159 | 2019 |
A full-image full-resolution end-to-end-trainable CNN framework for image forgery detection F Marra, D Gragnaniello, L Verdoliva, G Poggi IEEE Access 8, 133488-133502, 2020 | 112 | 2020 |
Blind PRNU-based image clustering for source identification F Marra, G Poggi, C Sansone, L Verdoliva IEEE Transactions on Information Forensics and Security 12 (9), 2197-2211, 2017 | 95 | 2017 |
A deep learning approach for iris sensor model identification F Marra, G Poggi, C Sansone, L Verdoliva Pattern Recognition Letters 113, 46-53, 2018 | 78 | 2018 |
A study of co-occurrence based local features for camera model identification F Marra, G Poggi, C Sansone, L Verdoliva Multimedia Tools and Applications 76, 4765-4781, 2017 | 66 | 2017 |
On the vulnerability of deep learning to adversarial attacks for camera model identification F Marra, D Gragnaniello, L Verdoliva Signal Processing: Image Communication 65, 240-248, 2018 | 61 | 2018 |
Evaluation of residual-based local features for camera model identification F Marra, G Poggi, C Sansone, L Verdoliva International Workshop on Recent Advances in Digital Security: Biometrics …, 2015 | 52 | 2015 |
Analysis of adversarial attacks against CNN-based image forgery detectors D Gragnaniello, F Marra, G Poggi, L Verdoliva European Signal Processing Conference (EUSIPCO), 2018 | 42 | 2018 |
Combining PRNU and noiseprint for robust and efficient device source identification D Cozzolino, F Marra, D Gragnaniello, G Poggi, L Verdoliva EURASIP Journal on Information Security 2020, 1-12, 2020 | 39 | 2020 |
Perceptual quality-preserving black-box attack against deep learning image classifiers D Gragnaniello, F Marra, L Verdoliva, G Poggi Pattern Recognition Letters 147, 142-149, 2021 | 37 | 2021 |
Detection of AI-generated synthetic faces D Gragnaniello, F Marra, L Verdoliva Handbook of digital face manipulation and detection: From deepfakes to …, 2022 | 25 | 2022 |
Correlation clustering for PRNU-based blind image source identification F Marra, G Poggi, C Sansone, L Verdoliva 2016 IEEE International Workshop on Information Forensics and Security (WIFS …, 2016 | 21 | 2016 |
Counter-forensics in machine learning based forgery detection F Marra, G Poggi, F Roli, C Sansone, L Verdoliva Media Watermarking, Security, and Forensics 2015 9409, 181-191, 2015 | 20 | 2015 |
PRNU-based forgery localization in a blind scenario D Cozzolino, F Marra, G Poggi, C Sansone, L Verdoliva Image Analysis and Processing-ICIAP 2017: 19th International Conference …, 2017 | 14 | 2017 |
Attacking the triangle test in sensor-based camera identification F Marra, F Roli, D Cozzolino, C Sansone, L Verdoliva ICIP, 5307-5311, 2014 | 14 | 2014 |
Virtual special issue on advances in digital security: Biometrics and forensics D Gragnaniello, CT Li, F Marra, D Riccio Pattern Recognition Letters 159, 220-221, 2022 | 4 | 2022 |
Guest editorial: Adversarial deep learning in biometrics & forensics R Chellappa, D Gragnaniello, CT Li, F Marra, R Singh Deakin University, 2021 | 1 | 2021 |