| 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 | 377 | 2018 |
| Do gans leave artificial fingerprints? F Marra, D Gragnaniello, L Verdoliva, G Poggi 2019 IEEE conference on multimedia information processing and retrieval …, 2019 | 323 | 2019 |
| 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 | 145 | 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 | 130 | 2021 |
| 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 | 95 | 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 | 93 | 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 | 65 | 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 | 55 | 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 | 41 | 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 | 36 | 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 | 31 | 2021 |
| 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 | 22 | 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 | 21 | 2015 |
| Detection of AI-generated synthetic faces D Gragnaniello, F Marra, L Verdoliva Handbook of digital face manipulation and detection: From deepfakes to …, 2022 | 19 | 2022 |
| 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 | 15 | 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 | 3 | 2022 |
| Guest editorial: Adversarial deep learning in biometrics & forensics R Chellappa, D Gragnaniello, CT Li, F Marra, R Singh Computer Vision and Image Understanding 208, 103227, 2021 | 1 | 2021 |
Luisa VerdolivaProfessor at University Federico II of Naples, Italy在 unina.it 的电子邮件经过验证
