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},

abstract = {Developing a reliable vision system is a fundamental challenge for robotic technologies (e.g., indoor service robots and outdoor autonomous robots) which can ensure reliable navigation even in challenging environments such as adverse weather conditions (e.g., fog, rain), poor lighting conditions (e.g., over/under exposure), or sensor degradation (e.g., blurring, noise), and can guarantee high performance in safety-critical functions. Current solutions proposed to improve model robustness usually rely on generic data augmentation techniques or employ costly test-time adaptation methods. In addition, most approaches focus on addressing a single vision task (typically, image recognition) utilising synthetic data. In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems. Our approach entails three key components: (i) a corruption type identification module, (ii) dynamic adjustment of normalization layer statistics based on identified corruption type, and (iii) real-time update of these statistics according to input data. PAN can integrate seamlessly with any convolutional model for enhanced accuracy in several robot vision tasks. In our experiments, PAN obtains robust performance improvement on challenging real-world corrupted image datasets (e.g., OpenLoris, ExDark, ACDC), where most of the current solutions tend to fail. Moreover, PAN outperforms the baseline models by 20-30% on synthetic benchmarks in object recognition tasks.},

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}

}