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@inproceedings{MRAC2024,

title = {W-TDL: Window-Based Temporal Deepfake Localization},

author = {Luka Dragar and Peter Rot and Peter Peer and Vitomir Štruc and Borut Batagelj},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/09/ACM_1M_DeepFakes.pdf},

year  = {2024},

date = {2024-11-01},

booktitle = {Proceedings of the 2nd International Workshop on Multimodal and Responsible Affective Computing (MRAC ’24), Proceedings of the 32nd ACM International Conference on Multimedia (MM’24)},

publisher = {ACM},

abstract = {The quality of synthetic data has advanced to such a degree of realism that distinguishing it from genuine data samples is increasingly challenging. Deepfake content, including images, videos, and audio, is often used maliciously, necessitating effective detection methods. While numerous competitions have propelled the development of deepfake detectors, a significant gap remains in accurately pinpointing the temporal boundaries of manipulations. Addressing this, we propose an approach for temporal deepfake localization (TDL) utilizing a window-based method for audio (W-TDL) and a complementary visual frame-based model. Our contributions include an effective method for detecting and localizing fake video and audio segments and addressing unbalanced training labels in spoofed audio datasets. Our approach leverages the EVA visual transformer for frame-level analysis and a modified TDL method for audio, achieving competitive results in the 1M-DeepFakes Detection Challenge. Comprehensive experiments on the AV-Deepfake1M dataset demonstrate the effectiveness of our method, providing an effective solution to detect and localize deepfake manipulations.},

keywords = {CNN, deepfake DAD, deepfakes, deeplearning, detection, localization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK_Marija_2021,

title = {A Comparative Study on Discriminative and One--Class Learning Models for Deepfake Detection},

author = {Marija Ivanovska and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2021/10/ERK_2021__A_Comparative_Study_of_Discriminative_and_One__Class_Learning_Models_for_Deepfake_Detection.pdf},

year  = {2021},

date = {2021-09-20},

booktitle = {Proceedings of ERK 2021},

pages = {1--4},

abstract = {Deepfakes or manipulated face images, where a donor's face is swapped with the face of a target person, have gained enormous popularity among the general public recently. With the advancements in  artificial intelligence and generative modeling  

such images can nowadays be easily generated and used to spread misinformation and harm individuals, businesses or society. As the tools for generating deepfakes are rapidly improving, it is critical for deepfake detection models to be able to recognize advanced, sophisticated data manipulations, including those that have not been seen during  training. In this paper, we explore the use of one--class learning models as an alternative to discriminative methods for the detection of deepfakes. We conduct a  comparative study with three popular deepfake datasets and investigate the performance of selected (discriminative and one-class) detection models in matched- and cross-dataset experiments. Our results show that disciminative models significantly outperform one-class models when training and testing data come from the same dataset, but degrade considerably when the characteristics of the testing data deviate from the training setting. In such cases, one-class models tend to generalize much better.},

keywords = {biometrics, comparative study, computer vision, deepfake detection, deepfakes, detection, face, one-class learning},

pubstate = {published},

tppubtype = {inproceedings}

}