Publications

@inproceedings{Deepfake_comp2023,

title = {DFGC-VRA: DeepFake Game Competition on Visual Realism Assessment},

author = {Bo Peng and Xianyun Sun and Caiyong Wang and Wei Wang and Jing Dong and Zhenan Sun and Rongyu Zhang and Heng Cong and Lingzhi Fu and Hao Wang and Yusheng Zhang and HanYuan Zhang and Xin Zhang and Boyuan Liu and Hefei Ling and Luka Dragar and Borut Batagelj and Peter Peer and Vitomir Struc and Xinghui Zhou and Kunlin Liu and Weitao Feng and Weiming Zhang and Haitao Wang and Wenxiu Diao},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/CameraReady-225.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},

pages = {1-9},

address = {Ljubljana, Slovenia},

abstract = {This paper presents the summary report on the DeepFake

Game Competition on Visual Realism Assessment (DFGCVRA).

Deep-learning based face-swap videos, also known

as deepfakes, are becoming more and more realistic and

deceiving. The malicious usage of these face-swap videos

has caused wide concerns. There is a ongoing deepfake

game between its creators and detectors, with the human in

the loop. The research community has been focusing on

the automatic detection of these fake videos, but the assessment

of their visual realism, as perceived by human

eyes, is still an unexplored dimension. Visual realism assessment,

or VRA, is essential for assessing the potential

impact that may be brought by a specific face-swap video,

and it is also useful as a quality metric to compare different

face-swap methods. This is the third edition of DFGC

competitions, which focuses on the new visual realism assessment

topic, different from previous ones that compete

creators versus detectors. With this competition, we conduct

a comprehensive study of the SOTA performance on

the new task. We also release our MindSpore codes to fur-

*Jing Dong (jdong@nlpr.ia.ac.cn) is the corresponding author.

ther facilitate research in this field (https://github.

com/bomb2peng/DFGC-VRA-benckmark).},

keywords = {competition IJCB, deepfake detection, deepfakes, face, realism assessment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SSBRC2023,

title = {Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023},

author = {Abhijit Das and Saurabh K Atreya and Aritra Mukherjee and Matej Vitek and Haiqing Li and Caiyong Wang and Zhao Guangzhe and Fadi Boutros and Patrick Siebke and Jan Niklas Kolf and Naser Damer and Ye Sun and Lu Hexin and Fab Aobo and You Sheng and Sabari Nathan and Suganya Ramamoorthy and Rampriya R S and Geetanjali G and Prinaka Sihag and Aditya Nigam and Peter Peer and Umapada Pal and Vitomir Struc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/CameraReady-233.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},

pages = {1-10},

address = {Ljubljana, Slovenia},

abstract = {This paper presents the summary of the Sclera Segmentation

and Joint Recognition Benchmarking Competition (SSRBC

2023) held in conjunction with IEEE International

Joint Conference on Biometrics (IJCB 2023). Different from

the previous editions of the competition, SSRBC 2023 not

only explored the performance of the latest and most advanced

sclera segmentation models, but also studied the impact

of segmentation quality on recognition performance.

Five groups took part in SSRBC 2023 and submitted a total

of six segmentation models and one recognition technique

for scoring. The submitted solutions included a wide

variety of conceptually diverse deep-learning models and

were rigorously tested on three publicly available datasets,

i.e., MASD, SBVPI and MOBIUS. Most of the segmentation

models achieved encouraging segmentation and recognition

performance. Most importantly, we observed that better

segmentation results always translate into better verification

performance.},

keywords = {biometrics, competition IJCB, computer vision, deep learning, sclera, sclera segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SSBC2020,

title = {SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment},

author = {M. Vitek and A. Das and Y. Pourcenoux and A. Missler and C. Paumier and S. Das and I. De Ghosh and D. R. Lucio and L. A. Zanlorensi Jr. and D. Menotti and F. Boutros and N. Damer and J. H. Grebe and A. Kuijper and J. Hu and Y. He and C. Wang and H. Liu and Y. Wang and Z. Sun and D. Osorio-Roig and C. Rathgeb and C. Busch and J. Tapia and A.~Valenzuela and G. Zampoukis and L. Tsochatzidis and I. Pratikakis and S. Nathan and R. Suganya and V. Mehta and A. Dhall and K. Raja and G. Gupta and J. N. Khiarak and M. Akbari-Shahper and F. Jaryani and M. Asgari-Chenaghlu and R. Vyas and S. Dakshit and S. Dakshit and P. Peer and U. Pal and V. Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/IJCB_SSBC_2020.pdf},

year  = {2020},

date = {2020-09-28},

booktitle = {International Joint Conference on Biometrics (IJCB 2020)},

pages = {1--10},

abstract = {The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent  performance across images captured with different mobile devices (with slight differences across devices), but struggled most with  low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. },

keywords = {biometrics, competition IJCB, ocular, sclera, segmentation, SSBC},

pubstate = {published},

tppubtype = {inproceedings}

}