\section{Related Work}
\label{sec:related_work}

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\subsection{Fraud Detection Under Distribution Shift and Campaign Structure}
Fraud detection systems operate in a hostile environment where attackers actively adapt to deployed defenses \cite{lunghi2023adversarial}. Fraud detection in production differs substantially from traditional calssification, because fraud strategies evolve over time, while labels are delayed and incomplete, and only a subset of alerts receives prompt expert review. Hence, models trained on historical fraud examples quickly become stale under distribution shift. For instance, credit card system focus on  concept drift adaption with delayed supervision information rather than  static classification on a fixed dataset \cite{Pozzolo2015}.

Classic classification is effective for closed-set, as conclusions drawn from small or simplified benchmarks may be transferrable to more realistic large-scale settings. Open-set recognition (OSR), however, addresses the scenario in which test-time inputs may not belong to any class observed during training \cite{bendale2016towards}. Handling test-time distribution shift is a central requirement for safe deployment (\cite{Mahdavi_2021, hendrycks2022scalingoutofdistributiondetectionrealworld, yang2022openoodbenchmarkinggeneralizedoutofdistribution, wang2024dissectingoutofdistributiondetectionopenset}). In the ID fraud domain, new layouts, fabrication pipelines, and manipulation strategies continually emerge, hence, effective systems must be capable of surfacing previously unseen fraud families.

In addition, frauds in the real world are structured and collective rather than independent across instances. In e-commerce and payment systems, graph-based and collective methods are developed to exploit inter-instance dependency, coordinated behavior, and group structure to identify fraud that may appear benign when each case is scored in isolation \cite{hooi2016fraudar, cao2018collective, yu2023}. Previous forensic-document research describes fraudulent identity documents as having a serial character, where multiple documents share a common source and can be detected as a series for intelligence and investigative purposes, including in cross-border settings \cite{MOULIN2022, MOULIN2024}.

\subsection{Self-Supervised Learning with Masked Image Modeling}
\paragraph{Self-supervised learning (SSL)}  has emerged as a powerful paradigm to learn transferable visual representations without human annotations. Early SSL approaches focused on handcrafted pretext tasks, such as predicting image rotations \cite{gidaris2018unsupervisedrepresentationlearningpredicting}, relative patch positions \cite{noroozi2017unsupervisedlearningvisualrepresentations}, image inpainting and context prediction \cite{pathak2016contextencodersfeaturelearning}, and split-brain autoencoding objectives \cite{zhang2017splitbrainautoencodersunsupervisedlearning}. 

Contrastive learning is a popular SSL framework. Methods such as SimCLR learn representations by maximizing agreement between differently augmented views of the same image while contrasting them against other samples in the batch \cite{chen2020simpleframeworkcontrastivelearning}. Momentum Contrast (MoCo) further improves scalability by introducing a momentum-updated encoder and a dynamic queue of negative samples, reducing the need for extremely large batch sizes \cite{he2020momentumcontrastunsupervisedvisual}.

Another important class of SSL methods is generative or reconstruction-based learning. Masked image modeling approaches, such as Masked Autoencoders (MAE), reconstruct missing image regions from partially observed inputs and have demonstrated strong scalability and transfer performance for Vision Transformers \cite{he2021maskedautoencodersscalablevision}. In contrast, SimMIM employs a standard Vision Transformer (ViT) that processes the full image—including masked tokens—and predicts the raw pixel values of masked patches using a lightweight reconstruction head \cite{Xie_2022_CVPR}.



\paragraph{DINO} Beyond contrastive learning with explicit negative samples, the Bootstrap Your Own Latent (BYOL) \cite{grill2020bootstraplatentnewapproach} model introduces a non-contrastive framework based on self-distillation. Building upon the self-distillation paradigm, DINO (self-DIstillation with NO labels) \cite{caron2021dino} extends BYOL by formulating the learning objective as a distribution matching problem. In DINO, a student network is trained to match the output distribution of a teacher network whose parameters are updated via an exponential moving average of the student weights. The model processes multiple augmented views of the same image and optimizes their alignment using a cross-entropy loss. The DINO yields semantically meaningful attention maps that often correspond to object-centric regions, enabling emergent object localization without supervision.

DINOv2 \cite{oquab2024dinov2} scaled to larger model sizes and significantly larger, curated and diversified training datasets. DINOv3 \cite{siméoni2025dinov3} further scaled the model sizes and training datasets, improved training stability, scalability, and representation quality. It has demonstrated strong performance across a wide range of downstream tasks, including image classification, object detection, and segmentation, often without task-specific fine-tuning. It has been widely adopted as a general-purpose visual backbone and foundation model for downstream tasks, e.g., medical vision tasks \cite{liu2026doesdinov3setnew}, robotics \cite{egbe2025dinov3diffusionpolicyselfsupervisedlarge} and object detection \cite{huang2025realtimeobjectdetectionmeets}.

\subsection{Deep Metric Learning for Fine-Grained Classification}

\paragraph{Deep metric learning} learns an embedding space where semantically similar samples are close and dissimilar ones are separated by large margins. It is particularly effective for fine-grained classification tasks where class distinctions are subtle, such as face recognition. Early approaches rely on pairwise or triplet objectives such as contrastive loss \cite{hadsell2006dimensionality} and triplet loss \cite{schroff2015facenet} that enforce similarity constraints between pairs or triplets of samples. However, these methods often converge slowly and require careful sample mining strategies \cite{wu2017sampling}. Recent work instead adopts classification-based metric learning losses that incorporate geometric constraints directly into the softmax objective, such as SphereFace \cite{liu2017sphereface}, CosFace \cite{wang2018cosface}, and ArcFace \cite{deng2019arcface}.



\paragraph{ArcFace Loss} introduces an additive angular margin to the softmax loss to enhance inter-class separability in the embedding space \cite{deng2019arcface}. Both feature vectors and classifier weights are normalized so that classification depends solely on angular distances on a hypersphere. Let $W_j$ denote the normalized weight vector for class $j$, and let $\theta_j$ be the angle between $z_i$ and $W_j$. The ArcFace loss is defined as
\begin{equation}
\mathcal{L}_{\text{ArcFace}} = -\frac{1}{N} \sum_{i=1}^{N} \log \frac{ e^{s\cos(\theta_{y_i}+m)} }{ e^{s\cos(\theta_{y_i}+m)} + \sum_{j \neq y_i} e^{s\cos(\theta_j)} }
\end{equation}
where $s$ is a feature scaling factor and $m$ is the additive angular margin. By explicitly increasing the angular distance between classes, ArcFace produces highly discriminative embeddings and has become a standard loss for large-scale face recognition.


\paragraph{Supervised Contrastive Learning(SupCon)} extends contrastive learning to the supervised setting by leveraging label information to define positive and negative pairs within a batch \cite{khosla2021supervisedcontrastivelearning} . For an anchor embedding $z_i$, the set of positives $P(i)$ includes all samples in the batch that share the same label. The SupCon loss is defined as
\begin{equation}
\mathcal{L}_{\text{SupCon}} = \sum_{i=1}^{N} \frac{-1}{|P(i)|} \sum_{p \in P(i)} \log \frac{ \exp(\mathbf{z}_i \cdot \mathbf{z}_p / \tau) }{ \sum_{a \neq i} \exp(\mathbf{z}_i \cdot \mathbf{z}_a / \tau) }
\end{equation}
where $|P(i)|$ is the number of positive samples in the batch, and $\tau$ is a temperature parameter controlling the concentration of the distribution. By pulling together embeddings from the same class and pushing apart embeddings from different classes, SupCon effectively utilizes multiple positive samples per anchor, leading to more stable and discriminative representations.


\paragraph{Center Loss} While margin-based softmax losses improve inter-class separation, they do not explicitly enforce intra-class compactness. Center Loss \cite{wen2016discriminativefeaturelearningapproach} addresses this limitation by learning a center $c_k$ for each class $k$ and minimizing the distance between features and their corresponding class centers:
\begin{equation}
\mathcal{L}_{\text{center}} = \frac{1}{2} \sum_{i=1}^{N} \left\| \mathbf{z}_i - \mathbf{c}_{y_i} \right\|_2^2
\end{equation}
During training, class centers are updated iteratively using mini-batch statistics. This formulation encourages features from the same class to form tight clusters in the embedding space.