@String(PAMI = {IEEE Trans. Pattern Anal. Mach. Intell.})
@String(IJCV = {Int. J. Comput. Vis.})
@String(CVPR= {IEEE Conf. Comput. Vis. Pattern Recog.})
@String(ICCV= {Int. Conf. Comput. Vis.})
@String(ECCV= {Eur. Conf. Comput. Vis.})
@String(NIPS= {Adv. Neural Inform. Process. Syst.})
@String(ICPR = {Int. Conf. Pattern Recog.})
@String(BMVC= {Brit. Mach. Vis. Conf.})
@String(TOG= {ACM Trans. Graph.})
@String(TIP  = {IEEE Trans. Image Process.})
@String(TVCG  = {IEEE Trans. Vis. Comput. Graph.})
@String(TMM  = {IEEE Trans. Multimedia})
@String(ACMMM= {ACM Int. Conf. Multimedia})
@String(ICME = {Int. Conf. Multimedia and Expo})
@String(ICASSP=	{ICASSP})
@String(ICIP = {IEEE Int. Conf. Image Process.})
@String(ACCV  = {ACCV})
@String(ICLR = {Int. Conf. Learn. Represent.})
@String(IJCAI = {IJCAI})
@String(PR   = {Pattern Recognition})
@String(AAAI = {AAAI})
@String(CVPRW= {IEEE Conf. Comput. Vis. Pattern Recog. Worksh.})
@String(CSVT = {IEEE Trans. Circuit Syst. Video Technol.})

@String(SPL	= {IEEE Sign. Process. Letters})
@String(VR   = {Vis. Res.})
@String(JOV	 = {J. Vis.})
@String(TVC  = {The Vis. Comput.})
@String(JCST  = {J. Comput. Sci. Tech.})
@String(CGF  = {Comput. Graph. Forum})
@String(CVM = {Computational Visual Media})


@String(PAMI  = {IEEE TPAMI})
@String(IJCV  = {IJCV})
@String(CVPR  = {CVPR})
@String(ICCV  = {ICCV})
@String(ECCV  = {ECCV})
@String(NIPS  = {NeurIPS})
@String(ICPR  = {ICPR})
@String(BMVC  =	{BMVC})
@String(TOG   = {ACM TOG})
@String(TIP   = {IEEE TIP})
@String(TVCG  = {IEEE TVCG})
@String(TCSVT = {IEEE TCSVT})
@String(TMM   =	{IEEE TMM})
@String(ACMMM = {ACM MM})
@String(ICME  =	{ICME})
@String(ICASSP=	{ICASSP})
@String(ICIP  = {ICIP})
@String(ACCV  = {ACCV})
@String(ICLR  = {ICLR})
@String(IJCAI = {IJCAI})
@String(PR = {PR})
@String(AAAI = {AAAI})
@String(CVPRW= {CVPRW})
@String(CSVT = {IEEE TCSVT})


@misc{khosla2021supervisedcontrastivelearning,
  title         = {Supervised Contrastive Learning},
  author        = {Prannay Khosla and Piotr Teterwak and Chen Wang and Aaron Sarna and Yonglong Tian and Phillip Isola and Aaron Maschinot and Ce Liu and Dilip Krishnan},
  year          = {2021},
  eprint        = {2004.11362},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2004.11362}
}


  @inproceedings{wen2016,
  author    = {Wen, Yandong
               and Zhang, Kaipeng
               and Li, Zhifeng
               and Qiao, Yu},
  editor    = {Leibe, Bastian
               and Matas, Jiri
               and Sebe, Nicu
               and Welling, Max},
  title     = {A Discriminative Feature Learning Approach for Deep Face Recognition},
  booktitle = {Computer Vision -- ECCV 2016},
  year      = {2016},
  publisher = {Springer International Publishing},
  address   = {Cham},
  pages     = {499--515},
  abstract  = {Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.},
  isbn      = {978-3-319-46478-7}
}



@inproceedings{Xie_2022_CVPR,
  author    = {Xie, Zhenda and Zhang, Zheng and Cao, Yue and Lin, Yutong and Bao, Jianmin and Yao, Zhuliang and Dai, Qi and Hu, Han},
  title     = {SimMIM: A Simple Framework for Masked Image Modeling},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2022},
  pages     = {9653-9663}
}

@inproceedings{Liu_2021_ICCV,
  author    = {Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  title     = {Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  month     = {October},
  year      = {2021},
  pages     = {10012-10022}
}

@inproceedings{He_2022_CVPR,
  author    = {He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll\'ar, Piotr and Girshick, Ross},
  title     = {Masked Autoencoders Are Scalable Vision Learners},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2022},
  pages     = {16000-16009}
}


@inproceedings{gidaris2018unsupervised,
  title     = {Unsupervised Representation Learning by Predicting Image Rotations},
  author    = {Gidaris, Spyros and Singh, Praveer and Komodakis, Nikos},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2018}
}

@inproceedings{caron2021dino,
  title     = {Emerging Properties in Self-Supervised Vision Transformers},
  author    = {Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J{\'e}gou, Herv{\'e} and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2021},
  pages     = {9650--9660}
}

@article{oquab2024dinov2,
  title   = {{DINO}v2: Learning Robust Visual Features without Supervision},
  author  = {Maxime Oquab and Timoth{\'e}e Darcet and Th{\'e}o Moutakanni and Huy V. Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel HAZIZA and Francisco Massa and Alaaeldin El-Nouby and Mido Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Herve Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
  journal = {Transactions on Machine Learning Research},
  issn    = {2835-8856},
  year    = {2024},
  note    = {Featured Certification}
}

@misc{siméoni2025dinov3,
  title         = {DINOv3},
  author        = {Oriane Siméoni and Huy V. Vo and Maximilian Seitzer and Federico Baldassarre and Maxime Oquab and Cijo Jose and Vasil Khalidov and Marc Szafraniec and Seungeun Yi and Michaël Ramamonjisoa and Francisco Massa and Daniel Haziza and Luca Wehrstedt and Jianyuan Wang and Timothée Darcet and Théo Moutakanni and Leonel Sentana and Claire Roberts and Andrea Vedaldi and Jamie Tolan and John Brandt and Camille Couprie and Julien Mairal and Hervé Jégou and Patrick Labatut and Piotr Bojanowski},
  year          = {2025},
  eprint        = {2508.10104},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2508.10104}
}


@article{darcet2023vitregisters,
  title   = {Vision Transformers Need Registers},
  author  = {Darcet, Timoth{\'e}e and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr},
  journal = {arXiv preprint arXiv:2309.16588},
  year    = {2023}
}



@misc{vidit2025fantasyiddatasetdetecting,
  title         = {FantasyID: A dataset for detecting digital manipulations of ID-documents},
  author        = {Vidit Vidit and Pavel Korshunov and Sébastien Marcel and Amir Mohammadi and Christophe Ecabert},
  year          = {2025},
  eprint        = {2507.20808},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2507.20808}
}

@misc{gidaris2018unsupervisedrepresentationlearningpredicting,
  title         = {Unsupervised Representation Learning by Predicting Image Rotations},
  author        = {Spyros Gidaris and Praveer Singh and Nikos Komodakis},
  year          = {2018},
  eprint        = {1803.07728},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1803.07728}
}


@misc{noroozi2017unsupervisedlearningvisualrepresentations,
  title         = {Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles},
  author        = {Mehdi Noroozi and Paolo Favaro},
  year          = {2017},
  eprint        = {1603.09246},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1603.09246}
}

@misc{pathak2016contextencodersfeaturelearning,
  title         = {Context Encoders: Feature Learning by Inpainting},
  author        = {Deepak Pathak and Philipp Krahenbuhl and Jeff Donahue and Trevor Darrell and Alexei A. Efros},
  year          = {2016},
  eprint        = {1604.07379},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1604.07379}
}

@misc{zhang2017splitbrainautoencodersunsupervisedlearning,
  title         = {Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction},
  author        = {Richard Zhang and Phillip Isola and Alexei A. Efros},
  year          = {2017},
  eprint        = {1611.09842},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1611.09842}
}

@misc{he2021maskedautoencodersscalablevision,
  title         = {Masked Autoencoders Are Scalable Vision Learners},
  author        = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
  year          = {2021},
  eprint        = {2111.06377},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2111.06377}
}


@misc{poole2019variationalboundsmutualinformation,
  title         = {On Variational Bounds of Mutual Information},
  author        = {Ben Poole and Sherjil Ozair and Aaron van den Oord and Alexander A. Alemi and George Tucker},
  year          = {2019},
  eprint        = {1905.06922},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/1905.06922}
}

@misc{chen2020simpleframeworkcontrastivelearning,
  title         = {A Simple Framework for Contrastive Learning of Visual Representations},
  author        = {Ting Chen and Simon Kornblith and Mohammad Norouzi and Geoffrey Hinton},
  year          = {2020},
  eprint        = {2002.05709},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2002.05709}
}

@misc{he2020momentumcontrastunsupervisedvisual,
  title         = {Momentum Contrast for Unsupervised Visual Representation Learning},
  author        = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick},
  year          = {2020},
  eprint        = {1911.05722},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1911.05722}
}

@misc{grill2020bootstraplatentnewapproach,
  title         = {Bootstrap your own latent: A new approach to self-supervised Learning},
  author        = {Jean-Bastien Grill and Florian Strub and Florent Altché and Corentin Tallec and Pierre H. Richemond and Elena Buchatskaya and Carl Doersch and Bernardo Avila Pires and Zhaohan Daniel Guo and Mohammad Gheshlaghi Azar and Bilal Piot and Koray Kavukcuoglu and Rémi Munos and Michal Valko},
  year          = {2020},
  eprint        = {2006.07733},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2006.07733}
}


@misc{liu2026doesdinov3setnew,
  title         = {Does DINOv3 Set a New Medical Vision Standard?},
  author        = {Che Liu and Yinda Chen and Haoyuan Shi and Jinpeng Lu and Bailiang Jian and Jiazhen Pan and Linghan Cai and Jiayi Wang and Jieming Yu and Ziqi Gao and Xiaoran Zhang and Long Bai and Yundi Zhang and Jun Li and Cosmin I. Bercea and Cheng Ouyang and Chen Chen and Zhiwei Xiong and Benedikt Wiestler and Christian Wachinger and James S. Duncan and Daniel Rueckert and Wenjia Bai and Rossella Arcucci},
  year          = {2026},
  eprint        = {2509.06467},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2509.06467}
}

@misc{egbe2025dinov3diffusionpolicyselfsupervisedlarge,
  title         = {DINOv3-Diffusion Policy: Self-Supervised Large Visual Model for Visuomotor Diffusion Policy Learning},
  author        = {ThankGod Egbe and Peng Wang and Zhihao Guo and Zidong Chen},
  year          = {2025},
  eprint        = {2509.17684},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2509.17684}
}

@misc{huang2025realtimeobjectdetectionmeets,
  title         = {Real-Time Object Detection Meets DINOv3},
  author        = {Shihua Huang and Yongjie Hou and Longfei Liu and Xuanlong Yu and Xi Shen},
  year          = {2025},
  eprint        = {2509.20787},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2509.20787}
}

@inproceedings{wen2016discriminativefeaturelearningapproach,
  author    = {Wen, Yandong
               and Zhang, Kaipeng
               and Li, Zhifeng
               and Qiao, Yu},
  editor    = {Leibe, Bastian
               and Matas, Jiri
               and Sebe, Nicu
               and Welling, Max},
  title     = {A Discriminative Feature Learning Approach for Deep Face Recognition},
  booktitle = {Computer Vision -- ECCV 2016},
  year      = {2016},
  publisher = {Springer International Publishing},
  address   = {Cham},
  pages     = {499--515},
  abstract  = {Convolutional neural networks (CNNs) have been widely used in computer vision community, significantly improving the state-of-the-art. In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model. In order to enhance the discriminative power of the deeply learned features, this paper proposes a new supervision signal, called center loss, for face recognition task. Specifically, the center loss simultaneously learns a center for deep features of each class and penalizes the distances between the deep features and their corresponding class centers. More importantly, we prove that the proposed center loss function is trainable and easy to optimize in the CNNs. With the joint supervision of softmax loss and center loss, we can train a robust CNNs to obtain the deep features with the two key learning objectives, inter-class dispension and intra-class compactness as much as possible, which are very essential to face recognition. It is encouraging to see that our CNNs (with such joint supervision) achieve the state-of-the-art accuracy on several important face recognition benchmarks, Labeled Faces in the Wild (LFW), YouTube Faces (YTF), and MegaFace Challenge. Especially, our new approach achieves the best results on MegaFace (the largest public domain face benchmark) under the protocol of small training set (contains under 500000 images and under 20000 persons), significantly improving the previous results and setting new state-of-the-art for both face recognition and face verification tasks.},
  isbn      = {978-3-319-46478-7}
}



@misc{george2025edgedochybridcnntransformermodel,
  title         = {EdgeDoc: Hybrid CNN-Transformer Model for Accurate Forgery Detection and Localization in ID Documents},
  author        = {Anjith George and Sebastien Marcel},
  year          = {2025},
  eprint        = {2508.16284},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2508.16284}
}

@inproceedings{Korshunov_2025_ICCV,
  author    = {Korshunov, Pavel and Vidit, Vidit and Mohammadi, Amir and Ecabert, Christophe and Shamoska, Nevena and Marcel, S\'ebastien and Yu, Zeqin and Tian, Ye and Ni, Jiangqun and Lazarevic, Lazar and Khizbullin, Renat and Evteeva, Anastasiia and Tochin, Alexey and Grishin, Aleksei and George, Anjith and Dealcala, Daniel and Endrei, Tamas and Mu\~noz-Haro, Javier and Tolosana, Ruben and Vera-Rodriguez, Ruben and Morales, Aythami and Fierrez, Julian and Cserey, Gy\"orgy and Sharma, Hardik and Chaudhary, Sachin and Dudhane, Akshay and Hambarde, Praful and Shukla, Amit and Shaily, Prateek and Kumar, Jayant and Hase, Ajinkya and Maurya, Satish and Sharma, Mridul and Dwivedi, Pallav},
  title     = {DeepID Challenge of Detecting Synthetic Manipulations in ID Documents},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
  month     = {October},
  year      = {2025},
  pages     = {510-519}
}

@inproceedings{samiei2025lightweight,
  title     = {Lightweight Embedded Framework for Edge Document Authenticity Detection},
  author    = {Salma Samiei},
  booktitle = {The Challenge of Detecting Synthetic Manipulations in ID Documents},
  year      = {2025},
  url       = {https://openreview.net/forum?id=yLN6DOlatM}
}

@misc{wong2025adcdnetrobustdocumentimage,
  title         = {ADCD-Net: Robust Document Image Forgery Localization via Adaptive DCT Feature and Hierarchical Content Disentanglement},
  author        = {Kahim Wong and Jicheng Zhou and Haiwei Wu and Yain-Whar Si and Jiantao Zhou},
  year          = {2025},
  eprint        = {2507.16397},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/2507.16397}
}

@inproceedings{Guillaro_2023_CVPR,
  author    = {Guillaro, Fabrizio and Cozzolino, Davide and Sud, Avneesh and Dufour, Nicholas and Verdoliva, Luisa},
  title     = {TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month     = {June},
  year      = {2023},
  pages     = {20606-20615}
}


@article{Polevoy2022DLC2021,
  author         = {Polevoy, Dmitry V. and Sigareva, Irina V. and Ershova, Daria M. and Arlazarov, Vladimir V. and Nikolaev, Dmitry P. and Ming, Zuheng and Luqman, Muhammad Muzzamil and Burie, Jean-Christophe},
  title          = {Document Liveness Challenge Dataset (DLC-2021)},
  journal        = {Journal of Imaging},
  volume         = {8},
  year           = {2022},
  number         = {7},
  article-number = {181},
  url            = {https://www.mdpi.com/2313-433X/8/7/181},
  pubmedid       = {35877624},
  issn           = {2313-433X},
  abstract       = {Various government and commercial services, including, but not limited to, e-government, fintech, banking, and sharing economy services, widely use smartphones to simplify service access and user authorization. Many organizations involved in these areas use identity document analysis systems in order to improve user personal-data-input processes. The tasks of such systems are not only ID document data recognition and extraction but also fraud prevention by detecting document forgery or by checking whether the document is genuine. Modern systems of this kind are often expected to operate in unconstrained environments. A significant amount of research has been published on the topic of mobile ID document analysis, but the main difficulty for such research is the lack of public datasets due to the fact that the subject is protected by security requirements. In this paper, we present the DLC-2021 dataset, which consists of 1424 video clips captured in a wide range of real-world conditions, focused on tasks relating to ID document forensics. The novelty of the dataset is that it contains shots from video with color laminated mock ID documents, color unlaminated copies, grayscale unlaminated copies, and screen recaptures of the documents. The proposed dataset complies with the GDPR because it contains images of synthetic IDs with generated owner photos and artificial personal information. For the presented dataset, benchmark baselines are provided for tasks such as screen recapture detection and glare detection. The data presented are openly available in Zenodo.},
  doi            = {10.3390/jimaging8070181}
}


@misc{cozzolino2018noiseprintcnnbasedcameramodel,
  title         = {Noiseprint: a CNN-based camera model fingerprint},
  author        = {Davide Cozzolino and Luisa Verdoliva},
  year          = {2018},
  eprint        = {1808.08396},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  url           = {https://arxiv.org/abs/1808.08396}
}


@misc{oord2019representationlearningcontrastivepredictive,
  title         = {Representation Learning with Contrastive Predictive Coding},
  author        = {Aaron van den Oord and Yazhe Li and Oriol Vinyals},
  year          = {2019},
  eprint        = {1807.03748},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/1807.03748}
}

@article{lunghi2023adversarial,
  title         = {Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives},
  author        = {Lunghi, Daniele and Simitsis, Alkis and Caelen, Olivier and Bontempi, Gianluca},
  year          = {2023},
  eprint        = {2307.01390},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2307.01390},
  doi           = {10.48550/arXiv.2307.01390}
}

@inproceedings{hadsell2006dimensionality,
  title={Dimensionality Reduction by Learning an Invariant Mapping},
  author={Hadsell, Raia and Chopra, Sumit and LeCun, Yann},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2006},
  url={https://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf}
}

@inproceedings{schroff2015facenet,
  title={FaceNet: A Unified Embedding for Face Recognition and Clustering},
  author={Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  url={https://arxiv.org/abs/1503.03832}
}

@inproceedings{wu2017sampling,
  title={Sampling Matters in Deep Embedding Learning},
  author={Wu, Chao-Yuan and Manmatha, R. and Smola, Alexander J. and Krahenbuhl, Philipp},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  url={https://arxiv.org/abs/1706.07567}
}

@inproceedings{liu2017sphereface,
  title={SphereFace: Deep Hypersphere Embedding for Face Recognition},
  author={Liu, Weiyang and Wen, Yandong and Yu, Zhiding and Li, Ming and Raj, Bhiksha and Song, Le},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  url={https://arxiv.org/abs/1704.08063}
}

@inproceedings{wang2018cosface,
  title={CosFace: Large Margin Cosine Loss for Deep Face Recognition},
  author={Wang, Hao and Wang, Yitong and Zhou, Zheng and Ji, Xing and Gong, Dihong and Zhou, Jingchao and Li, Zhifeng and Liu, Wei},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018},
  url={https://arxiv.org/abs/1801.09414}
}

@inproceedings{deng2019arcface,
  title={ArcFace: Additive Angular Margin Loss for Deep Face Recognition},
  author={Deng, Jiankang and Guo, Jia and Xue, Niannan and Zafeiriou, Stefanos},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019},
  url={https://arxiv.org/abs/1801.07698}
}

@inproceedings{deng2009imagenet,
  title={ImageNet: A Large-Scale Hierarchical Image Database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2009},
  pages={248--255},
  doi={10.1109/CVPR.2009.5206848},
  url={https://www.image-net.org/static_files/papers/imagenet_cvpr09.pdf}
}

@article{howard2018universal,
  title={Universal Language Model Fine-tuning for Text Classification},
  author={Howard, Jeremy and Ruder, Sebastian},
  journal={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL)},
  year={2018},
  pages={328--339},
  doi={10.18653/v1/P18-1031},
  archivePrefix={arXiv},
  eprint={1801.06146},
  url={https://arxiv.org/abs/1801.06146}
}

@article{montone2017gradual,
  title={Gradual Tuning: a Better Way of Fine Tuning the Parameters of a Deep Neural Network},
  author={Montone, Guido and Rizzo, Giuseppe and Morisio, Maurizio},
  journal={arXiv preprint arXiv:1711.10177},
  year={2017},
  archivePrefix={arXiv},
  eprint={1711.10177},
  url={https://arxiv.org/abs/1711.10177}
}

@INPROCEEDINGS{Pozzolo2015,
  author={Dal Pozzolo, Andrea and Boracchi, Giacomo and Caelen, Olivier and Alippi, Cesare and Bontempi, Gianluca},
  booktitle={2015 International Joint Conference on Neural Networks (IJCNN)}, 
  title={Credit card fraud detection and concept-drift adaptation with delayed supervised information}, 
  year={2015},
  volume={},
  number={},
  pages={1-8},
  keywords={Europe;Fraud Detection;Concept Drift;Unbalanced Data;Data Streams;Anomaly Detection},
  doi={10.1109/IJCNN.2015.7280527}}

  @inproceedings{bendale2016towards,
  title     = {Towards Open Set Deep Networks},
  author    = {Bendale, Abhijit and Boult, Terrance E.},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {1563--1572},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.173}
}

@inproceedings{Mahdavi_2021,
   title={A Survey on Open Set Recognition},
   url={http://dx.doi.org/10.1109/AIKE52691.2021.00013},
   DOI={10.1109/aike52691.2021.00013},
   booktitle={2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)},
   publisher={IEEE},
   author={Mahdavi, Atefeh and Carvalho, Marco},
   year={2021},
   month=dec, pages={37–44} 
}

@misc{hendrycks2022scalingoutofdistributiondetectionrealworld,
      title={Scaling Out-of-Distribution Detection for Real-World Settings}, 
      author={Dan Hendrycks and Steven Basart and Mantas Mazeika and Andy Zou and Joe Kwon and Mohammadreza Mostajabi and Jacob Steinhardt and Dawn Song},
      year={2022},
      eprint={1911.11132},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1911.11132}, 
}

@misc{yang2022openoodbenchmarkinggeneralizedoutofdistribution,
      title={OpenOOD: Benchmarking Generalized Out-of-Distribution Detection}, 
      author={Jingkang Yang and Pengyun Wang and Dejian Zou and Zitang Zhou and Kunyuan Ding and Wenxuan Peng and Haoqi Wang and Guangyao Chen and Bo Li and Yiyou Sun and Xuefeng Du and Kaiyang Zhou and Wayne Zhang and Dan Hendrycks and Yixuan Li and Ziwei Liu},
      year={2022},
      eprint={2210.07242},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2210.07242}, 
}

@misc{wang2024dissectingoutofdistributiondetectionopenset,
      title={Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks}, 
      author={Hongjun Wang and Sagar Vaze and Kai Han},
      year={2024},
      eprint={2408.16757},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2408.16757}, 
}

@inproceedings{hooi2016fraudar,
  title     = {FRAUDAR: Bounding Graph Fraud in the Face of Camouflage},
  author    = {Hooi, Bryan and Song, Hyun Ah and Beutel, Alex and Shah, Neil and Shin, Kijung and Faloutsos, Christos},
  booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)},
  pages     = {895--904},
  year      = {2016},
  doi       = {10.1145/2939672.2939747},
  url       = {https://www.kdd.org/kdd2016/papers/files/rfp0110-hooiA.pdf}
}

@inproceedings{cao2018collective,
  title     = {Collective Fraud Detection Capturing Inter-Transaction Dependency},
  author    = {Cao, Bokai and Mao, Mia and Viidu, Siim and Yu, Philip S.},
  booktitle = {Proceedings of the KDD 2017 Workshop on Anomaly Detection in Finance},
  series    = {Proceedings of Machine Learning Research},
  volume    = {71},
  pages     = {66--75},
  year      = {2018},
  publisher = {PMLR},
  url       = {https://proceedings.mlr.press/v71/cao18a.html}
}

@inproceedings{yu2023,
  title     = {Group-based Fraud Detection Network on e-Commerce Platforms},
  author    = {Yu, Jianke and Wang, Hanchen and Wang, Xiaoyang and Li, Zhao and Qin, Lu and Zhang, Wenjie and Liao, Jian and Zhang, Ying},
  booktitle = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2023},
  url       = {https://cgi.cse.unsw.edu.au/~zhangw/files/2023_KDD_paper.pdf}
}

@article{elsevier2022fraud,
  title   = {Fraud Detection in Online Platforms: A Survey},
  journal = {International Journal of Information Management Data Insights},
  year    = {2022},
  publisher = {Elsevier},
  doi     = {10.1016/j.jjimei.2022.100111}
}
@article{MOULIN2022,
title = {An efficient method to detect series of fraudulent identity documents based on digitised forensic data},
journal = {Science \& Justice},
volume = {62},
number = {5},
pages = {610-620},
year = {2022},
issn = {1355-0306},
doi = {https://doi.org/10.1016/j.scijus.2022.09.003},
url = {https://www.sciencedirect.com/science/article/pii/S1355030622001113},
author = {Solène {Lugon Moulin} and Céline Weyermann and Simon Baechler},
keywords = {Security, Forgery, Counterfeit, Questioned document, Forensic, Profiling},
abstract = {Document fraud is a transnational form of crime, and its serial character has already been highlighted. To combat this phenomenon, the Interstate Database of Fraudulent Identity Documents (BIDIF) has been created and implemented in Switzerland. It supports the comparison of documents and the detection of series, i.e., documents that share a common source. To efficiently use such a system, forensic document examiners would benefit from a harmonised and proven profiling method. Thus, the aim of this study is to develop a method for comparing documents and establishing series. The method is meant to improve the detection capabilities of forensic document examiners operating BIDIF or engaged in the profiling of fraudulent documents. First, a method based on the visual characteristics of digitised images of fraudulent identity documents has been developed. Subsequently, the method was qualitatively and quantitatively evaluated using four tests. The first test verified the ability of the method to detect pre-existing series. The second test checked the capability of the method to detect links amongst isolated documents. Finally, two further tests were carried out to compare the method impact on the successful detection of series. These tests were carried out by professional forensic document examiners and Master students in forensic science, respectively. This allowed a comparison of the method influence on series detection. The method allowed a significant increase in the number of series and links detected, while also decreasing the occurrence of false negatives and false positives. Furthermore, links were more rapidly detected.}
}

@article{MOULIN2024,
title = {Cross-border forensic profiling of fraudulent identity and travel documents: A pilot project between France and Switzerland},
journal = {Science \& Justice},
volume = {64},
number = {2},
pages = {202-209},
year = {2024},
issn = {1355-0306},
doi = {https://doi.org/10.1016/j.scijus.2024.01.003},
url = {https://www.sciencedirect.com/science/article/pii/S1355030624000030},
author = {Solène Lugon Moulin and Emre Ertan and Didier Martin and Simon Baechler},
keywords = {Forgery, Counterfeit, Document examination, Police cooperation, Security},
abstract = {The serial character of document fraud and its connection to organised crime groups who produce, sell and/or use fraudulent documents is a challenge for security and crime fighting. As a response, the added value of forensic intelligence is increasingly recognised. Using a forensic profiling method and a dedicated system deployed in Switzerland, document examiners can detect series (i.e., documents that share a common source) of fraudulent documents conveniently and efficiently. This detection can trigger or orientate investigations, supports crime intelligence efforts, and facilitates cross-jurisdictional cooperation. This study aims to assess the suitability of the forensic profiling system for international purpose and the efficiency of the method to detect cross-border series. The forensic profiling system has been deployed in France in the framework of a cross-border pilot project conducted by the School of Criminal Justice from the University of Lausanne and the French National Police (Division Nationale de Lutte contre la Fraude Documentaire et à l'Identité) over the period July 2019–May 2020. Data from the Swiss and French forensic profiling systems were compared to each other to detect cross-border series. The study sought to create operating conditions as close as possible to the real-life conditions of the profiling systems. The results are extremely positive both quantitatively and qualitatively. They demonstrate the benefit of setting up a systematic exchange of forensic data issued from profiling systems for fraudulent identity documents between France and Switzerland, let alone between any other countries. The results open up a very promising prospect for a sustained operational implementation by the police services of both countries and the extension of the exchanges internationally.}
}

@article{guan2024idnet,
  title   = {IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},
  author  = {Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},
  journal = {arXiv preprint arXiv:2408.01690},
  year    = {2024},
  url     = {https://arxiv.org/abs/2408.01690}
}