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ICAISG 2026: International Conference on Artificial Intelligence Security and Governance

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ICAISG
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投稿締切日:
2026-09-15 Extended
通知日:
2026-10-15
開催日:
2026-11-20
開催地:
Hangzhou, China
開催回数:
2
主催者:
閲覧: 1282   フォロー: 0   参加: 0

会伴インデックス (CP-I)

40.8 / 100
全 5,687 件中 第 4,853 位 · 上位 86%

セキュリティ・プライバシー 分野 361 件中 第 310 位 人工知能・機械学習 分野 741 件中 第 620 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
30
コミュニティの注目度 (10%)
21
公開情報の充実度 (15%)
35

使用した入力: 確認できる開催回数：2 · 過去 24 か月にこのページを開いた研究者：16 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-27

論文募集

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
最終更新：Dunn Carl （）

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