Organizations: National School of Development, Peking University, Beijing 100871, China · College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China · School of Foreign Studies, Beijing Language and Culture University, Beijing 100083, China · Artificial Intelligence Research Institute, Tsinghua University, Beijing 100084, China
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed and coordinated across heterogeneous modalities, challenging platform governance and early risk identification. We propose MM-FGDNet, a data-driven multimodal framework for detecting abnormal behavior in large-scale live-streaming environments from complementary temporal-evolution and group-structure perspectives. A cross-modal temporal alignment module maps video, text, audio, and user behavior into a unified temporal semantic space. A temporal fraud-pattern module captures the progression from weak early signals to abrupt outbreaks, while a cooperative manipulation module identifies coordinated interactions among organized user groups and automated accounts. Experiments on real-world multi-platform live-streaming e-commerce datasets show that MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689, while reducing false alarms. Ablation studies validate the contribution of each module, and cross-domain experiments demonstrate stable generalization to new streamers, product categories, and platforms. These results indicate that MM-FGDNet provides an effective and scalable solution for proactive detection of coordinated abnormal behavior in live-streaming systems.
Figures & tables
Data Modality
Source
Quantity
Granularity
Live-stream video frames
Live e-commerce platforms
3.2 M frames
Frame-level
Audio segments
Live-stream audio tracks
18,400 clips
Segment-level
Comments & barrage text
Live comment streams
12.6 M entries
Message-level
Transaction records
Tipping and order logs
1.8 M events
Event-level
User interaction sequences
Likes, follows, shares
9.4 M actions
Action-level
Table 1: Overview of the multimodal fraud detection dataset.
Figure 1: Illustration of the cross-modal temporal alignment module.
Figure 2: This figure illustrates the architecture and information flow of the temporal fraud pattern modeling module.
Figure 3: This figure depicts the overall pipeline and structural design of the cooperative manipulation detection module.
Method
AUC ↑
Precision ↑
Recall ↑
F1 ↑
FDR ↑
FAR ↓
EDS ↑
CDGS ↑
Isolation Forest
0.781
0.702
0.648
0.674
0.648
0.142
0.412
0.681
One-Class SVM
0.793
0.718
0.662
0.689
0.662
0.136
0.438
0.695
LSTM
0.834
0.756
0.721
0.738
0.721
0.121
0.502
0.734
GRU
0.842
0.764
0.728
0.746
0.728
0.118
0.517
0.742
Transformer (temporal)
0.867
0.791
0.756
0.773
0.756
0.106
0.561
0.771
GNN-based Fraud Detection
0.872
0.798
0.761
0.779
0.761
0.103
0.548
0.786
Table 2: Overall performance comparison with baseline methods.
Figure 4: Overall performance comparison with baseline methods.
Variant
AUC ↑
F1 ↑
FDR ↑
FAR ↓
EDS ↑
CDGS ↑
Full MM-FGDNet
0.927
0.847
0.834
0.071
0.689
0.872
w / o Cross-Modal Alignment
0.898
0.812
0.798
0.089
0.602
0.821
w / o Temporal Fraud Modeling
0.883
0.799
0.782
0.093
0.531
0.814
w / o Cooperative Detection
0.892
0.807
0.791
0.086
0.578
0.793
w / o Diffusion Augmentation
0.901
0.818
0.804
0.082
0.614
0.839
Table 3: Ablation study of different components in MM-FGDNet.
Figure 5: Radar chart illustrating the comprehensive ablation study results, comparing the full MM-FGDNet framework against four variants with specific core components removed. The axes represent six key evaluation metrics: AUC, F1-Score, Fraud Detection Rate (FDR), False Alarm Rate (FAR, plotted inversely where outward indicates lower FAR), Early Detection Score (EDS), and Cross-Domain Generalization Score (CDGS).
Target Scenario
Method
F1 ↑
AUC ↑
EDS ↑
New Streamer
Transformer (temporal)
0.742
0.851
0.512
Multimodal Fusion (MMBT)
0.769
0.873
0.548
GNN-based Fraud Detection
0.779
0.881
0.536
MM-FGDNet
0.816
0.914
0.662
New Product Category
Transformer (temporal)
0.736
0.846
0.498
Multimodal Fusion (MMBT)
0.761
0.869
0.531
Table 4: Cross-domain generalization performance under different target scenarios.
Figure 6: Cross-domain generalization performance under different target scenarios.