Conference Information

ICAISG 2026: International Conference on Artificial Intelligence Security and Governance

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ICAISG
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Submission Date:
2026-09-15 Extended
Notification Date:
2026-10-15
Conference Date:
2026-11-20
Location:
Hangzhou, China
Years:
2
Organizer:
Viewed: 1279   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

40.8 / 100
Ranked #4,853 of 5,687 conferences · Top 86%

#310 of 361 in Security & Privacy #620 of 741 in Artificial Intelligence & Machine Learning

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
30
Community attention (10%)
21
Public record completeness (15%)
35

Inputs used: Editions on record: 2 · Researchers who opened this page in the past 24 months: 16

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-27

Call For Papers

ICAISG 2026 ( International Conference on Artificial Intelligence Security and Governance) is an academic conference held in Hangzhou, China on 2026-11-20. The paper submission deadline is 2026-09-15 (extended). Acceptance notifications are sent on 2026-10-15.

Papers in the main technical program must describe high-quality, original research. Topics of interest include all aspects of artificial intelligence and software engineering including, but not limited to Track 1: Content Generation and Tampering Content Detection ▪ Media Manipulation Detection and Localization ▪ Deepfake Forgery Detection and Mitigation ▪ Authenticity Assessment of AI-Generated Media (Images, Videos, Audio, Text) ▪ Approximate Reasoning Track 2: Traceability and Provenance Analysis of Synthetic Content ▪ Source Device Attribution of Synthetic Media ▪ Generative Model Attribution (GANs, Diffusion Models) ▪ Identity Provenance in AI-Generated Content Track 3: Security of Large Language Models ▪ Adversarial Attacks and Defense Strategies for LLMs ▪ Jailbreaking Attacks and Prompt Injection Mitigation ▪ Security Risks in Knowledge Distillation Pipelines ▪ Monitoring Malicious Adaptation of Open-Source LLMs ▪ Detection and Mitigation of Hallucinations in LLMs ▪ Content Integrity Assurance in Multimodal LLMs Track 4: Data Privacy Protection ▪ Privacy Leakage in Federated Learning Systems ▪ Privacy-Preserving Data Anonymization Techniques ▪ Secure Multi-Party Computation Frameworks ▪ Ethical Implications of Synthetic Data Generation ▪ Countermeasures Against AI-Driven Data Reconstruction Track 5: AI-Driven Cybersecurity ▪ AI-Powered Threat Detection and Incident Response ▪ Automated Vulnerability Discovery and Exploitation ▪ AI in Offensive and Defensive Network Operations ▪ Collaborative Threat Intelligence Sharing via AI ▪ Quantum Computing Threats to AI Security Protocols Track 6: Automated Adversarial Testing and Validation ▪ Generation and Application of Adversarial Examples ▪ AI-Based Attack Simulation and Penetration Testing ▪ Automated Verification of AI System Robustness Track 7: Security of AI-Enabled IoT Systems ▪ Privacy and Integrity of IoT Data Streams ▪ Defense Mechanisms for AI-Enhanced IoT Networks Track 8: Biometric Security and AI ▪ Privacy-Preserving Biometric Data Management ▪ Anti-Spoofing Techniques for Biometric Systems ▪ AI-Augmented Biometric Authentication Track 9: Ethical AI and Regulatory Compliance ▪ Accountability in AI Decision-Making Processes ▪ Bias Detection and Fairness in Algorithmic Systems ▪ Legal and Compliance Frameworks for AI Deployment ▪ Inclusive Algorithm Design for Diverse Populations ▪ Moral Responsibility in Autonomous Decision Systems Track 10: Emerging Trends in AI Security ▪ Security Challenges of Cutting-Edge AI Technologies ▪ Novel Defense Paradigms for Future AI Systems ▪ Strategic Roadmap for Long-Term AI Security Track 11: Explainable and Transparent AI ▪ Standardization of Black-Box Model Interpretability ▪ High-Stakes Applications of Transparent AI (e.g., Legal, Financial) ▪ Quantifying User Trust in AI-Driven Decisions ▪ Balancing Explainability and Model Efficiency ▪ Cross-Cultural Adaptation of Explainability Tools Track 12: Adversarial Robustness in AI Systems ▪ Generation and Detection of Adversarial Perturbations ▪ Impact Analysis of Adversarial Attacks on AI Models ▪ Enhancing System Robustness and Fault Tolerance ▪ Threat Modeling for GAN-Enabled AI Systems ▪ Real-World Adversarial Attack Scenarios ▪ Game-Theoretic Approaches to Defense Mechanisms ▪ Vulnerability Assessment of Multimodal AI Models Track 13: Public Engagement and AI Literacy ▪ Global Educational Frameworks for AI Security ▪ Digital Platforms for Civic Participation in AI Governance
Last updated by Dunn Carl on

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