Najibul Haque Sarker — accepted AI-conference papers

2026

Immunizing Models Against Harmful Long-Horizon Fine-Tuning via Contractive Optimization Dynamics

Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Ali Asgarov, Chia-Wei Tang, Alvi Md Ishmam, Chris Thomas

CVPR 2026

Fine-tuning has become the default way to adapt powerful foundation models, but this also enables low-cost repurposing for harmful objectives. Existing immunization methods try to optimize local geometry or simulate short attacker horizons, and penalize observed loss drops. However, in practice, dow

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2026

LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models

Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Chris Thomas

AAAI 2026technical

Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to process multiple images as inputs. However, the vulnerabilities of multi-image MLLMs remain unexplored. Existing adversarial attacks focus on single-i

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2025

SONICS: Synthetic Or Not - Identifying Counterfeit Songs

Md Awsafur Rahman, Zaber Ibn Abdul Hakim, Najibul Haque Sarker, Bishmoy Paul, Shaikh Anowarul Fattah

ICLR 2025poster

The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song d…

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