MuST-GAN MFAS is a novel model in multi-modal Face Anti-Spoofing, effectively addressing challenges in adaptability to unseen attacks. Utilizing modality-specific encoders and Swin Transformer layers, the model disentangles spoof traces through cross-modal attention mechanisms and a StyleSwin transformer-based generator. Its bidirectional adversarial learning approach ensures identity consistency, intensity, center, and classification considerations. Rigorous evaluations demonstrate MuST-GAN MFAS's superiority over existing frameworks, showcasing remarkable performance across diverse modal samples. This model makes a substantial contribution to face anti-spoofing by emphasizing the importance of learning multi-semantic spoof traces for improved generalization and adaptability.

1. Doctoral Student
Reported By: Zain Ul Abideen
Dec 21, 2023
Central South University, Changsha, Hunan, China

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Self Introduction
Eagerly seeking a Ph.D., I hail from a Pakistani village, blessed with numerous successes. Grateful for the opportunity
to interview at top-tier institutions, my passion lies in contributing to impactful research, particularly in Artificial
Intelligence and Computer Vision
Honor and Awards
Awarded a Fully Funded Master’s University Scholarship at Central South University
Sep 01, 2021 – June 2024
Date of Birth: 31-01-1997
Nationality: Pakistani
Name: Zain Ul Abideen

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Educational Background
Sep. 2021 Jun. 2024: School of Computer Science and Technology,
Central South University China, Master's (CS), MuST-GAN MFAS: Multi-Semantic
Spoof Tracer GAN with Transformer Layers for Multi-modal Face Anti-Spoofing,
supervised by Professor Shu Liu.
Sep. 2016-Jul. 2020: Department of Computer Science and Technology, Government
College University, Pakistan, BS(CS, “AI based GCUF Assistant Chatbot” Supervised
by Professor Rao Iqbal.

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Publications
• Ul Abideen, Z. and Liu, S. (2023). GAN MFAS: Multi-Semantic Spoof Tracer GAN
with Transformer Layers for Multi-modal Face Anti-Spoofing
• Shahzad, I. and Ul Abideen, Z. (2023). Hybrid Learning to Classify Autistic and
Non-autistic Face.
• Abbas, W. and Ul Abideen, Z. (2023) Classify Attire detection using Optimized
Hybrid Learning
• Hussain, T. Ul Abideen, Z. , (2023) A Hybrid Deep Learning Approach for Improved
E-Learning based on Automatic Learning Style.

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Work Experience
• Nov 2020-Feb 2022: Data Analyst at E-commerce company, Running E-commerce
stores in all over the world. Location: Lahore, Pakistan.
• Sep 2018-May 2020: Sales Agent at UK based company Providing Electricity and
Gas Services. Location: Faisalabad, Pakistan.

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Skills
• Programming Languages: Proficient in Python at Visual Studio Code, PyCharm,
Google Colab and Kaggle Environments.
• LaTeX: Experienced in using LaTeX for academic paper writing.
• Graphic Design: Skilled in visio for creating figures, diagrams, and graphics to
enhance research presentations and publications

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Introduction
Fig. 1 Disentangled
Spoofs on Cefa
Casia Surf Cefa Dataset
MuST-GAN MFAS is a novel model in multi-modal Face Anti-Spoofing,
effectively addressing challenges in adaptability to unseen attacks. Utilizing
modality-specific encoders and Swin Transformer layers, the model
disentangles spoof traces through cross-modal attention mechanisms and a
StyleSwin transformer-based generator. Its bidirectional adversarial learning
approach ensures identity consistency, intensity, center, and classification
considerations. Rigorous evaluations demonstrate MuST-GAN MFAS's
superiority over existing frameworks, showcasing remarkable performance
across diverse modal samples. This model makes a substantial contribution to
face anti-spoofing by emphasizing the importance of learning multi-semantic
spoof traces for improved generalization and adaptability.

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• Traditional machine learning FAS methods
• CNN-based FAS methods
• Vision Transformer based FAS methods
• GAN based FAS methods
Related Work
Fig. 2 Typical GAN

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•Spoof Trace Generation:
• StyleSwin-based generator extracts complementary features for precise spoof
detection.
• Modality-specific encoders and decoders separate live features from spoof traces.
•Multi-Modal Fusion:
• Novel method fuses RGB, depth, and infrared modalities.
• Cross-modal attention mechanisms and style-based architecture enhance resilience.
• Extracts textural, geometric, and thermal patterns for efficient detection of emerging
attacks.
•Comprehensive Training:
• Bidirectional adversarial learning, consistency loss, identity, intensity, center, and
classification losses.
• Ensures effective separation of spoof traces while preserving identity information
and modality consistency.
Academic Innovations

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•MuST-GAN MFAS enhances model robustness against spoofing attacks by integrating
RGB, depth, and infrared modalities. Modality-specific encoders tailored for each image type
disentangle multi-semantic spoof traces. The model employs cross-modal attention
mechanisms to selectively emphasize relevant information and a style-based architecture for
efficient fusion. A double attention mechanism in transformer layers captures fine structures
and coarse geometry simultaneously. The integration of absolute position knowledge, lost in
window-based transformers, enhances generation quality. The model's training involves
various losses, guiding the process for improved accuracy, and a classification network
combines spoof traces from all modalities for the final decision.
MuST GAN FAS

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MuST GAN FAS
Fig. 3 Overall Architecture

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Cross-modality and Feature Fusion
The proposed MuST-GAN MFAS employs a cross-modality feature fusion process for
enhanced spoof trace generation in face anti-spoofing. This process involves Feature
Separation (FS) and Feature Aggregation (FA) stages, utilizing cross-modality channel
attention and spatial-wise gates. The fusion methodology integrates RGB, depth, and
infrared modalities, filtering influential channels with learned attention weights. The
refined features undergo re-computation and resizing steps for. A spatial aggregation
step, utilizing transformer layers of global information, focuses on spoof-related
regions induced by attacks. The final reinforced features represent a seamless
integration of RGB, depth, and infrared aspects, enhancing disentanglement
performance across modalities in the MuST-GAN MFAS transformer.

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Cross Modality and Feature Fusion
Fig. 4 Cross Modality and Feature Fusion

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Training Process and Loss Functions
The proposed approach employs bidirectional adversarial learning for disentangling spoof traces, utilizing live parts'
data dispersion similarities and decomposed spoof patterns as building blocks. Multi-scale discriminators, inspired
by PatchGAN structure, assess features for adversarial learning, incorporating transformer layers. Identity
consistency loss ensures preservation of identity properties during reconstruction, while intensity loss regularizes
spoof trace intensity. Center loss optimizes feature distribution for improved generalization, and the classification
loss ensures correct live and spoof sample classification. Training involves three steps: generator, discriminator, and
consistency supervision. The overall loss balances various components, enhancing the model's ability to generate and
classify spoof traces.

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Training Process and Loss Functions
Fig. 5 Training Process and Loss Functions

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Experimental Settings
•MuST-GAN MFAS Multi-Encoder and Multi-Decoder model training was
conducted on a NVIDIA GeForce RTX 3090Ti GPU.
•A batch size of 16 and the Adam optimizer were utilized for 300 iterations.
•The initial learning rate was set to 5× 10−5.
•Hyperparameters {α1, α2, α3, α4, α5, α6, α7, α8} were configured as {0.25, 100,
1, 100, 1, 10, 1, 1}.
•To address class imbalance, distinct learning rates for the generator and
discriminator were applied: 5 × 10−5 and 2 × 10−4, respectively.

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Experimental Settings
Experimental Evaluations:
•Extensive experiments conducted on three diverse multi-modal datasets.
•Assessment of MuST-GAN MFAS using intra-testing experiments.
•Metrics include APCER, BPCER, ACER, and TPR@FPR=10^-4 for Casia Surf.
•Cross-testing experiments measure HTER based.
•Datasets intentionally selected for comprehensive examination across difficulties and
scenarios.

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Experimental Settings
Datasets Utilized:
•CASIA-Surf :
• Large-scale, multi-modal dataset for face anti-spoofing.
• Over 50,000 videos from 10,000 subjects.
• Includes RGB, depth, and infrared modalities.
•CASIA-SURF CeFA :
• Encompasses 2D and 3D attacks, considering rigid and silicon masks.
• RGB, depth, and IR modalities.
• Provides five protocols for varied conditions.
•WMCA :
• Wide Multi-Channel Presentation Attack database.
• Developed at Idiap within "IARPA BATL" and "H2020 TESLA" projects.
• 1941 short video recordings across 72 identities.
• Channels include color, depth, infrared, and thermal.
• Utilized for cross-testing MuST-GAN MFAS for generalization capabilities in diverse tasks.
Fig. 6 Cefa Dataset

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Experimental Settings
•Testing Scenarios:
• Investigated two testing scenarios:
• Common practice aligning testing with training techniques.
• Flexible scenario allowing testers to use individual or combined techniques.
•Protocols and Test Scenarios:
• Implemented specific protocols aligned with training techniques for intra-dataset
evaluation.
• Explored adaptability across datasets, emphasizing effectiveness within CASIA-
SURF and CeFA.
• Extended evaluation to cross-dataset testing, particularly with WMCA.
• WMCA introduces grandtest and unseen attack protocols, evaluating generalization in
various scenarios.

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Experimental Settings
•MuST-GAN MFAS Evaluation:
• Perfected through extensive training on CASIA-SURF and CeFA datasets, leveraging
RGB, Depth, and Infrared modalities.
• Thorough evaluation in intra-dataset scenarios, focusing on CASIA-SURF and CeFA
intricacies.
• Transitioned seamlessly into cross-dataset testing, evaluating generalization on
WMCA with distinct protocols.
•Comparison with SOTA Frameworks:
• Contrasted MuST-GAN MFAS with state-of-the-art frameworks.
• Engaged in a comprehensive discussion beyond insignificant performance metrics.
• Carefully evaluated the effectiveness of MuST-GAN MFAS, securing its position
among the state-of-the-art face anti-spoofing systems.

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Results

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Results

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Results
•Ablation Study Summary:Baseline:
• No disentanglement, classification network trained and
evaluated using unaltered RGB-D-ir images.
• Disentanglement process significantly improves performance
compared to the baseline.
• Single modality usage (RGB Only, DEP Only, IR Only)
surpasses baseline in terms of ACER.
•RGB Only, DEP Only, and IR Only:
• Each disentanglement network branch modeled separately
without cross-modality integration.
• Noticeable performance enhancements when employing
individual modalities for decision-making.
• DEP Only performance exceeds RGB Only and IR Only,
emphasizing the benefit of using depth information alone.

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Results
•Feature Concatenation (RGB & DEP & IR):
Cross-modality feature fusion implemented through vanilla
concatenation.
Outperforms feature concatenation method, showcasing the
effectiveness of cross-modality fusion in reinforcing
correlations.
SA-Gate:
Feature fusion module replaced with the original version of SA-
Gate.
Model exhibits additional enhancements in ACER compared to
SA-Gate, highlighting the superior efficacy of the proposed
fusion strategy.

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Conclusion
In conclusion, MuST-GAN MFAS emerges as a novel force in Face Anti-Spoofing (FAS).
Leveraging adversarial disentangled representation learning and cross-modality feature fusion, our
model excels in detecting a diverse range of fraudulent scenarios. The innovative spoof trace
generator, with double attention enhancement, achieves unprecedented levels of trace synthesis.
Extensive experiments showcase the model's effectiveness against both traditional and unseen
attacks. MuST-GAN MFAS not only advances FAS solutions but also paves the way for exploring
transformers in facial recognition research. Its versatile defense mechanisms establish it as a
steadfast guardian against evolving threats, symbolizing a creative advancement at the intersection
of innovation and security. As the curtain descends, MuST-GAN MFAS stands as a motivational
work for future endeavors in the dynamic landscape of face anti-spoofing.

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35. 感谢各位老师聆听！
THANKS FOR ALL