Multimedia forensics

@inproceedings{nokey,

title = {Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics},

author = {Elena Camuffo and Umberto Michieli and Simone Milani and Ji Joong Moon and Mete Ozay},

editor = {IEEE},

url = {https://arxiv.org/abs/2407.06450},

year  = {2024},

date = {2024-06-30},

urldate = {2024-06-30},

booktitle = { IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

journal = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Camuffo2023d,

title = {FFT-based Selection And Optimization Of Statistics For Robust Recognition Of Severely Corrupted Images},

author = {Elena Camuffo and Umberto Michieli and Ji Joong Moon and Daehyun Kim and Mete Ozay},

url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10447639},

year  = {2024},

date = {2024-01-26},

urldate = {2024-01-26},

booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

abstract = {Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corrupted images. Our method (FROST) employs high-frequency features to detect input image corruption type, and select layer-wise feature normalization statistics. FROST provides the state-of-the-art results for different models and datasets, outperforming competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of 40.9% mCE on severe corruptions.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mari2022,

title = {The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection},

author = {Daniele Mari and Federica Latora and Simone Milani},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {2022 IEEE International Workshop on Information Forensics and Security (WIFS)},

pages = {1--6},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rossi2021,

title = {Neural network for denoising and reading degraded license plates},

author = {Gianmaria Rossi and Marco Fontani and Simone Milani},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI},

pages = {484--499},

organization = {Springer International Publishing},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}