Publications
Journals
Salvi, Davide, Paolo Bestagini, and Stefano Tubaro. "Towards Frequency Band Explainability in Synthetic Speech Detection." In 2023 31st European Signal Processing Conference (EUSIPCO), pp. 620-624. IEEE, 2023.
@InProceedings{salvi2023towards,
author = {Salvi, Davide and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {European Signal Processing Conference (EUSIPCO)},
title = {Towards Frequency Band Explainability in Synthetic Speech Detection},
year = {2023},
pages = {620-624},
doi = {10.23919/EUSIPCO58844.2023.10289804},
groups = {audio, forensics},
keywords = {Training;Forensics;Focusing;Europe;Detectors;Media;Signal processing;Multimedia Forensics;Audio;Synthetic Speech;Explainability},
}
Salvi, Davide, Paolo Bestagini, and Stefano Tubaro. "Reliability Estimation for Synthetic Speech Detection." In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5. IEEE, 2023.
@InProceedings{salvi2023reliability,
author = {Salvi, Davide and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title = {Reliability Estimation for Synthetic Speech Detection},
year = {2023},
pages = {1-5},
doi = {10.1109/ICASSP49357.2023.10095524},
groups = {audio, forensics},
keywords = {Training;Deepfakes;Forensics;Estimation;Detectors;Signal processing;Reliability;Audio Forensics;Speech;Deepfake;Reliability},
}
Salvi, Davide, Paolo Bestagini, and Stefano Tubaro. "Synthetic speech detection through audio folding." In Proceedings of the 2nd ACM International Workshop on Multimedia AI against Disinformation, pp. 3-9. 2023.
InProceedings{salvi2023synthetic,
author = {Salvi, Davide and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {ACM International Workshop on Multimedia AI against Disinformation},
title = {Synthetic Speech Detection through Audio Folding},
year = {2023},
address = {New York, NY, USA},
pages = {3–9},
publisher = {Association for Computing Machinery},
series = {MAD '23},
abstract = {In the field of synthetic speech generation, recent advancements in deep learning and speech synthesis methods have enabled the possibility of creating highly realistic fake speech tracks that are difficult to distinguish from real ones. Since the malicious use of these data can lead to dangerous consequences, the audio forensics community has focused on developing synthetic speech detectors to determine the authenticity of speech tracks. In this work we focus on the wide class of detectors that analyze audio streams on a frame-by-frame basis. We propose a technique to reduce the inference time of these detectors by relying on the fact that it is possible to mix multiple audio frames in a single one (i.e., in the same way a mono track is obtained from a stereo one). We test the proposed audio folding technique on speech tracks obtained from the ASVspoof 2019 dataset. The technique proves effective with both entirely and partially fake speech tracks and shows remarkable results, reducing processing time down to 25\%.},
doi = {10.1145/3592572.3592844},
groups = {audio, forensics},
isbn = {9798400701870},
keywords = {Audio Folding, Audio Forensics, Digital signal processing, Synthetic Speech},
location = {Thessaloniki, Greece},
numpages = {7},
url = {https://doi.org/10.1145/3592572.3592844},
}
Salvi, Davide, Honggu Liu, Sara Mandelli, Paolo Bestagini, Wenbo Zhou, Weiming Zhang, and Stefano Tubaro. "A robust approach to multimodal deepfake detection." Journal of Imaging 9, no. 6 (2023): 122.
@Article{salvi2023robust,
author = {Salvi, Davide and Liu, Honggu and Mandelli, Sara and Bestagini, Paolo and Zhou, Wenbo and Zhang, Weiming and Tubaro, Stefano},
journal = {Journal of Imaging},
title = {A Robust Approach to Multimodal Deepfake Detection},
year = {2023},
issn = {2313-433X},
number = {6},
volume = {9},
abstract = {The widespread use of deep learning techniques for creating realistic synthetic media, commonly known as deepfakes, poses a significant threat to individuals, organizations, and society. As the malicious use of these data could lead to unpleasant situations, it is becoming crucial to distinguish between authentic and fake media. Nonetheless, though deepfake generation systems can create convincing images and audio, they may struggle to maintain consistency across different data modalities, such as producing a realistic video sequence where both visual frames and speech are fake and consistent one with the other. Moreover, these systems may not accurately reproduce semantic and timely accurate aspects. All these elements can be exploited to perform a robust detection of fake content. In this paper, we propose a novel approach for detecting deepfake video sequences by leveraging data multimodality. Our method extracts audio-visual features from the input video over time and analyzes them using time-aware neural networks. We exploit both the video and audio modalities to leverage the inconsistencies between and within them, enhancing the final detection performance. The peculiarity of the proposed method is that we never train on multimodal deepfake data, but on disjoint monomodal datasets which contain visual-only or audio-only deepfakes. This frees us from leveraging multimodal datasets during training, which is desirable given their lack in the literature. Moreover, at test time, it allows to evaluate the robustness of our proposed detector on unseen multimodal deepfakes. We test different fusion techniques between data modalities and investigate which one leads to more robust predictions by the developed detectors. Our results indicate that a multimodal approach is more effective than a monomodal one, even if trained on disjoint monomodal datasets.},
article-number = {122},
doi = {10.3390/jimaging9060122},
groups = {forensics},
pubmedid = {37367470},
url = {https://www.mdpi.com/2313-433X/9/6/122},
}
Salvi, Davide, Mirco Pezzoli, Sara Mandelli, Paolo Bestagini, and Stefano Tubaro. "Are you Really Alone? Detecting the use of Speech Separation Techniques on Audio Recordings." In 2023 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1-6. IEEE, 2023.
@InProceedings{salvi2023are,
author = {Salvi, Davide and Pezzoli, Mirco and Mandelli, Sara and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {IEEE International Workshop on Information Forensics and Security (WIFS)},
title = {Are you Really Alone? Detecting the use of Speech Separation Techniques on Audio Recordings},
year = {2023},
pages = {1-6},
doi = {10.1109/WIFS58808.2023.10374717},
groups = {audio, forensics},
keywords = {Training;Fourier transforms;Forensics;Detectors;Media;Audio recording;Signal representation;Forensics;Audio;Speech;Speech Separation},
}
Salvi, Davide, Brian Hosler, Paolo Bestagini, Matthew C. Stamm, and Stefano Tubaro. "TIMIT-TTS: A text-to-speech dataset for multimodal synthetic media detection." IEEE Access (2023).
@Article{salvi2023timit,
author = {Salvi, Davide and Hosler, Brian and Bestagini, Paolo and Stamm, Matthew C. and Tubaro, Stefano},
journal = {IEEE Access},
title = {TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection},
year = {2023},
pages = {50851-50866},
volume = {11},
doi = {10.1109/ACCESS.2023.3276480},
groups = {audio, forensics},
keywords = {Deepfakes;Detectors;Audio systems;Speech synthesis;Media;Forensics;Visualization;Audio;multimodal;deepfake;forensics;synthetic speech;text-to-speech;TIMIT},
}
Wang, J., Tondi, B., & Barni, M. (2023, June). “Classification of synthetic facial attributes by means of hybrid classification/localization patch-based analysis”. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE, 04-10 June 2023, Rhodes Island, Greece
@inproceedings{wang2023classification,
title={Classification of synthetic facial attributes by means of hybrid classification/localization patch-based analysis},
author={Wang, Jun and Tondi, Benedetta and Barni, Mauro},
booktitle={ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2023},
organization={IEEE}
}
Wang, J., Alamayreh, O., Tondi, B., & Barni, M. (2023). “Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture”. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 953-962)
@inproceedings{wang2023open,
title={Open set classification of gan-based image manipulations via a vit-based hybrid architecture},
author={Wang, Jun and Alamayreh, Omran and Tondi, Benedetta and Barni, Mauro},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={953--962},
year={2023}
}
Abady, L., Wang, J., Tondi, B., & Barni, M. (2023). “A Siamese-based Verification System for Open-set Architecture Attribution of Synthetic Images”. Submitted to Pattern Recognition Letters, Vol 180, April 2024, Pages 75-81
@article{abady2024siamese, title={A siamese-based verification system for open-set architecture attribution of synthetic images},author={Abady, Lydia and Wang, Jun and Tondi, Benedetta and Barni, Mauro},journal={Pattern Recognition Letters},volume={180},pages={75--81}, year={2024},publisher={Elsevier}
}
Montibeller, Andrea, and Fernando Pérez-González. "An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections." IEEE Transactions on Information Forensics and Security 2023.
}
Salvi, Davide, Paolo Bestagini, and Stefano Tubaro. "Exploring the synthetic speech attribution problem through data-driven detectors." In 2022 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1-6. IEEE, 2022.
@InProceedings{salvi2022exploring,
author = {Salvi, Davide and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {IEEE International Workshop on Information Forensics and Security (WIFS)},
title = {Exploring the Synthetic Speech Attribution Problem Through Data-Driven Detectors},
year = {2022},
pages = {1-6},
doi = {10.1109/WIFS55849.2022.9975440},
groups = {audio, forensics},
keywords = {Deepfakes;Adaptation models;Forensics;Conferences;Detectors;Speech synthesis;Security;Forensics;Audio;Speech;Deepfake;Attribution},
}
Conti, Emanuele, Davide Salvi, Clara Borrelli, Brian Hosler, Paolo Bestagini, Fabio Antonacci, Augusto Sarti, Matthew C. Stamm, and Stefano Tubaro. "Deepfake speech detection through emotion recognition: a semantic approach." In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8962-8966. IEEE, 2022.
@InProceedings{conti2022deepfake,
author = {Conti, Emanuele and Salvi, Davide and Borrelli, Clara and Hosler, Brian and Bestagini, Paolo and Antonacci, Fabio and Sarti, Augusto and Stamm, Matthew C. and Tubaro, Stefano},
booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title = {Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach},
year = {2022},
pages = {8962-8966},
doi = {10.1109/ICASSP43922.2022.9747186},
groups = {audio, forensics},
keywords = {Voice activity detection;Emotion recognition;Semantics;Transfer learning;Signal processing algorithms;Speech recognition;Streaming media;deepfake;audio forensics;deep learning},
}
Attorresi, Luigi, Davide Salvi, Clara Borrelli, Paolo Bestagini, and Stefano Tubaro. "Combining automatic speaker verification and prosody analysis for synthetic speech detection." In International Conference on Pattern Recognition, pp. 247-263. Cham: Springer Nature Switzerland, 2022.
@InProceedings{attorresi2023combining,
author = {Attorresi, Luigi and Salvi, Davide and Borrelli, Clara and Bestagini, Paolo and Tubaro, Stefano},
booktitle = {International Conference on Pattern Recognition (ICPR)},
title = {Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection},
year = {2023},
address = {Cham},
editor = {Rousseau, Jean-Jacques and Kapralos, Bill},
pages = {247--263},
publisher = {Springer Nature Switzerland},
abstract = {The rapid spread of media content synthesis technology and the potentially damaging impact of audio and video deepfakes on people's lives have raised the need to implement systems able to detect these forgeries automatically. In this work we present a novel approach for synthetic speech detection, exploiting the combination of two high-level semantic properties of the human voice. On one side, we focus on speaker identity cues and represent them as speaker embeddings extracted using a state-of-the-art method for the automatic speaker verification task. On the other side, voice prosody, intended as variations in rhythm, pitch or accent in speech, is extracted through a specialized encoder. We show that the combination of these two embeddings fed to a supervised binary classifier allows the detection of deepfake speech generated with both Text-to-Speech and Voice Conversion techniques. Our results show improvements over the considered baselines, good generalization properties over multiple datasets and robustness to audio compression.},
groups = {audio, forensics},
isbn = {978-3-031-37742-6},
}
Wang, J., Alamayreh, O., Tondi, B., & Barni, M. (2022, June). “An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities”. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) (pp. 1-5). IEEE.
@inproceedings{wang2022architecture,
title={An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities},
author={Wang, Jun and Alamayreh, Omran and Tondi, Benedetta and Barni, Mauro},
booktitle={2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)},
pages={1--5},
year={2022},
organization={IEEE}
}
D. Lin, B. Tondi, B. Li and M. Barni, "Exploiting temporal information to prevent the transferability of adversarial examples against deep fake detectors," 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022, pp. 1-8
@inproceedings{lin2022exploiting,
title={Exploiting temporal information to prevent the transferability of adversarial examples against deep fake detectors},
author={Lin, Dongdong and Tondi, Benedetta and Li, Bin and Barni, Mauro},
booktitle={2022 IEEE International Joint Conference on Biometrics (IJCB)},
pages={1--8},
year={2022},
organization={IEEE}
}
Wang, J., Tondi, B., and Barni, M. (2022). “An eyes-based siamese neural network for the detection of gan-generated face images”. Frontiers in Signal Processing, 2, 918725, DOI: 10.3389/frsip.2022.918725.
F. Marcon, C. Pasquini, G. Boato, “Detection of manipulated face videos over social networks: a large scale study”, Journal of Imaging, 2021
M. Iuliani, M. Fontani and A. Piva, "A Leak in PRNU Based Source Identification—Questioning Fingerprint Uniqueness," IEEE Access, vol. 9, pp. 52455-52463, 2021
Daniele Baracchi, Dasara Shullani, Massimo Iuliani, Damiano Giani, Alessandro Piva, "Camera Obscura: Exploiting in-camera processing for image counter forensics", Forensic Science International: Digital Investigation, Vol. 38, 2021
D Cozzolino, A Rössler, J Thies, M Nießner, L Verdoliva, "ID-Reveal: Identity-aware DeepFake Video Detection", International Conference on Computer Vision, October 2021
D Gragnaniello, F Marra, G Poggi, L Verdoliva, "Perceptual quality-preserving black-box attack against deep learning image classifiers", Pattern Recognition Letters, vol. 147, 2021
C. Pasquini, I. Amerini, G. Boato, "Media Forensics on Social Media and Web Platforms: a Survey", EURASIP Journal on Information Security, vol. 4, 2021.
F. Lago, C. Pasquini, R. Boehme, H. Dumont, V. Goffaux, G. Boato, "More real than real: a study on human visual perception of synthetic faces", IEEE Signal Processing Magazine, 2021
C. Borrelli, P. Bestagini, F. Antonacci, A. Sarti, and S. Tubaro., “Synthetic speech detection through short-term and long-term prediction traces”, EURASIP Journal on Information Security, 2021(1): 2. 2021
M. Bonomi, C. Pasquini, G. Boato, "Dynamic texture analysis for detecting fake faces in video sequences", Journal of Visual Communication and Image Representation, 2021
M. Barni, Q-T. Phan, B. Tondi, “Copy Move Source-Target Disambiguation through Multi-Branch CNNs”, IEEE Transactions on Information Forensics and Security, vol. 16, 2021, pp. 1825-1840
D. Cozzolino, F. Marra, D. Gragnaniello, G. Poggi, L. Verdoliva, “Combining PRNU and noiseprint for robust and efficient device source identification”, EURASIP Journal on Information Security, pp. 1-12, 2020
Y. Niu, B. Tondi, Y. Zhao, M. Barni, “Primary Quantization Matrix Estimation of Double Compressed JPEG Images via CNN”, IEEE Signal Processing Letters, vol. 27, pp. 191-195, 2020
P. Yang, D. Baracchi, M. Iuliani, D. Shullani, R. Ni, Y. Zhao, A. Piva, "Efficient Video Integrity Analysis Through Container Characterization", IEEE Journal of Selected Topics in Signal Processing, 2020, 14.5: 947-954.
S. Autherith, C. Pasquini, “Detecting Morphing Attacks through Face Geometry Features”, Journal of Imaging, 2020
Sara Mandelli, Davide Cozzolino, Paolo Bestagini, Luisa Verdoliva, Stefano Tubaro, "CNN-based fast source device identification", IEEE Signal Processing Letters, vol. 27, pp. 1285-1289, 2020.
Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, Giovanni Poggi, "A Full-Image Full-Resolution End-to-End-Trainable CNN Framework for Image Forgery Detection", IEEE Access, vol. 8, pp. 1333488-133502, 2020.
P. Yang, D. Baracchi, R. Ni, Y. Zhao, F. Argenti, A. Piva, "A survey of deep learning-based source image forensics". Journal of Imaging, 6(3), 9, (2020).
Luisa Verdoliva, "Media Forensics and Deepfakes: an overview", IEEE Journal of Selected Topics in Signal Processing, 2020 (in press).
Mattia Bonomi, Giulia Boato, "Digital human face detection in video sequences via a physiological signal analysis", SPIE Journal of Electronic Imaging, no. 1, vol. 29, pp. 1-10, 2020.
Giulia Boato, Duc-Tien Dang-Nguyen, Francesco G. B. De Natale, "Morphological Filter Detector for Image Forensics Applications", IEEE Access 8: 13549-13560, 2020.
Workshops/Conferences
E. D. Cannas, S. Baireddy, E. R. Bartusiak, S. K. Yarlagadda, D. Mas Montserrat, P. Bestagini, S. Tubaro, E. J. Delp, "Open-Set Source Attribution for Panchromatic Satellite Imagery", IEEE International Conference on Image Processing (ICIP), 2021
@INPROCEEDINGS{cannas2021,
author={Cannas, E. D. and Baireddy, S. and Bartusiak, E. R. and Yarlagadda, S. K. and Montserrat, D. Mas and Bestagini, P. and Tubaro, S. and Delp, E. J.},
booktitle={IEEE International Conference on Image Processing (ICIP)},
title={Open-Set Source Attribution for Panchromatic Satellite Imagery},
year={2021},
volume={},
number={},
pages={3038-3042}}
Michele Pilia, Sara Mandelli, Paolo Bestagini, Stefano Tubaro, "Time Scaling Detection and Estimation in Audio Recordings", IEEE International Workshop on Information Forensics and Security (WIFS), 2021
@INPROCEEDINGS{audio2021,
author={Pilia, Michele and Mandelli, Sara and Bestagini, Paolo and Tubaro, Stefano},
booktitle={IEEE International Workshop on Information Forensics and Security (WIFS)},
title={Time Scaling Detection and Estimation in Audio Recordings},
year={2021},
volume={},
number={},
pages={1-6},
}
C. Albisani, M. Iuliani, A. Piva, "Checking PRNU Usability on Modern Devices", IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021
@INPROCEEDINGS{9413611,
author={Albisani, C. and Iuliani, M. and Piva, A.},
booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Checking PRNU Usability on Modern Devices},
year={2021},
volume={},
number={},
pages={2535-2539}}
D Cozzolino, J Thies, A Rössler, M Nießner, L Verdoliva, "SpoC: Spoofing camera fingerprints", IEEE Computer Vision and Pattern Recognition (CVPR) Workshops, 2021
@INPROCEEDINGS{9522784, author={Cozzolino, Davide and Thies, Justus and Rössler, Andreas and Nießner, Matthias and Verdoliva, Luisa}, booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, title={SpoC: Spoofing Camera Fingerprints}, year={2021}, volume={}, number={}, pages={990-1000}, doi={10.1109/CVPRW53098.2021.00110}}
Hosler, B.; Salvi, D.; Murray, A.; Antonacci, F.; Bestagini, P.; Tubaro, S.; and Stamm, M. C., “Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies”, In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021
@INPROCEEDINGS{HSMABTS2021,
author={Hosler, Brian and Salvi, Davide and Murray, Anthony and Antonacci, Fabio and Bestagini, Paolo and Tubaro, Stefano and Stamm, Matthew C.},
booktitle={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
title={Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies},
year={2021},
volume={},
number={},
pages={1013-1022},
}
O. Alamayreh, M. Barni, "Detection of GAN-synthesized street videos", European Signal Processing Conference (EUSIPCO), 2021
D Gragnaniello, D Cozzolino, F Marra, G Poggi, L Verdoliva, "Are GAN generated images easy to detect? A critical analysis of the state-of-the-art", IEEE International Conference on Multimedia & Expo, 2021
N. Bonettini, P. Bestagini, S. Milani, S. Tubaro, “On the use of Benford's law to detect GAN-generated images”, International Conference on Pattern Recognition (ICPR), 2020
@INPROCEEDINGS{BBMT2020,
author={N. Bonettini and P. Bestagini and S. Milani and S. Tubaro},
booktitle={Proceedings of ICPR . MMForWild Workshop},
title={On the use of Benford's law to detect GAN-generated images},
year={2020}}
W. Li, B. Tondi, R. Ni, M. Barni, “Increased-confidence Adversarial Examples for Deep Learning Counter-Forensics”, Proc. of MMForWILD 2020, International workshop on Multimedia Forensics in the Wild, 11 January 2021, Milan.
@INPROCEEDINGS{LTNB2020,
author={W. Li and B. Tondi and R. Ni and M. Barni},
booktitle={Proceedings of ICPR . MMForWild Workshop},
title={Increased-confidence Adversarial Examples for Deep Learning Counter-Forensics},
year={2020}}
N. Bonettini, E. D. Cannas, S. Mandelli, L. Bondi, P. Bestagini, S. Tubaro, “Video Face Manipulation Detection Through Ensemble of CNNs”, International Conference on Pattern Recognition (ICPR), 2020
@INPROCEEDINGS{BCMBBT2020,
author={W. Li and B. Tondi and R. Ni and M. Barni},
booktitle={Proceedings of ICPR . MMForWild Workshop},
title={Video Face Manipulation Detection Through Ensemble of CNNs},
year={2020}}
S. Mandelli, F. Argenti, P. Bestagini, M. Iuliani, A. Piva, S. Tubaro, "A Modified Fourier-Mellin Approach for Source Device Identification on Stabilized Videos", IEEE International Conference on Image Processing (ICIP), 2020
@INPROCEEDINGS{MABIPT2020,
author={S. {Mandelli} and F. {Argenti} and P. {Bestagini} and M. {Iuliani} and A. {Piva} and S. {Tubaro}},
booktitle={IEEE International Conference on Image Processing (ICIP)},
title={A Modified Fourier-Mellin Approach For Source Device Identification On Stabilized Videos},
year={2020},
volume={},
number={},
pages={1266-1270}}
M. Barni, E. Nowroozi, B. Tondi, B. Zhang, “Effectiveness of Deep Random Feature Selection for Securing Image Manipulation Detectors Against Adversarial Examples”, Proc. of ICASSP 2020, 4-8 May 2020.
@INPROCEEDINGS{BNTZ2020,
author={M. {Barni} and E. {Nowroozi} and B. {Tondi} and B. {Zhang}},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Effectiveness of Random Deep Feature Selection for Securing Image Manipulation Detectors Against Adversarial Examples},
year={2020},
volume={},
number={},
pages={2977-2981}}
L. Bondi, E. D. Cannas, P. Bestagini, S. Tubaro, "Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection", IEEE International Workshop on Information Forensics and Security (WIFS), 2020
@INPROCEEDINGS{BCBT2020,
author={L. {Bondi} and E. {Daniele Cannas} and P. {Bestagini} and S. {Tubaro}},
booktitle={2020 IEEE International Workshop on Information Forensics and Security (WIFS)},
title={Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection},
year={2020},
volume={},
number={},
pages={1-6}}
M. Barni, K. Kallas, E. Nowroozi, Benedetta Tondi, “CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis”, Proc. of WIFS 2020.
@INPROCEEDINGS{BKNT2020, author={M. {Barni} and K. {Kallas} and E. {Nowroozi} and B. {Tondi}}, booktitle={2020 IEEE International Workshop on Information Forensics and Security (WIFS)}, title={CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis}, year={2020}, volume={}, number={}, pages={1-6}}
S. Mandelli, N. Bonettini, P. Bestagini, and S. Tubaro, “Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision,” in IEEE International Workshop on Information Forensics and Security (WIFS), 2020.
@INPROCEEDINGS{MBBT2020, author={S. {Mandelli} and N. {Bonettini} and P. {Bestagini} and S. {Tubaro}}, booktitle={2020 IEEE International Workshop on Information Forensics and Security (WIFS)}, title={Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision}, year={2020}, volume={}, number={}, pages={1-6}}
Boato, Giulia, et al. "TrueFace: A dataset for the detection of synthetic face images from social networks." 2022 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2022.
@inproceedings{boato2022trueface,
title={TrueFace: A dataset for the detection of synthetic face images from social networks},
author={Boato, Giulia and Pasquini, Cecilia and Stefani, Antonio L and Verde, Sebastiano and Miorandi, Daniele},
booktitle={2022 IEEE International Joint Conference on Biometrics (IJCB)},
pages={1--7},
year={2022},
organization={IEEE}
}
Book chapters
R. Tolosana, C. Rathgeb, R. Vera-Rodriguez, C. Busch, L. Verdoliva, S. Lyu et al. Future Trends in Digital Face Manipulation and Detection, Handbook of Digital Face Manipulation and Detection, pp. 463-482, March 2022
D. Cozzolino, L. Verdoliva, "Multimedia Forensics Before the Deep Learning Era", Handbook of Digital Face Manipulation and Detection, pp. 45-67, March 2022
D. Gragnaniello, F. Marra, L Verdoliva, "Detection of AI-Generated Synthetic Faces", Handbook of Digital Face Manipulation and Detection, pp. 191-212, March 2022