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Faisal Alhwikem

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Open access Oct 2026

An Optimized Audio–Visual Deepfake Detection Framework Using Mamba Vision Transformers with Linear Complexity

The rise of advanced deepfake technologies presents new challenges to media authenticity, making it harder to spot manipulated videos through traditional approaches. Current approaches have a quadratic time complexity O(n2), poor cross-dataset generalization with 20 to 35% accuracy degradation, and inadequate audio–vis...

Aisha Arain, Bushra Naz, Sania Bhatti et al. · 0 citations
Open access Sep 2026

ContextGuard-RAG: Contextual Integrity-Aware Retrieval-Augmented Generation with Multi-Agent Privacy Enforcement for Sensitive Document Question Answering

Large language model (LLM)-powered retrieval-augmented generation (RAG) systems significantly reduce factual errors in question answering, but they present a novel and under-investigated attack surface: factual information in retrieved text can be exposed in ways that do not align with the disclosure norms of the infor...

Faisal Alhwikem, A. Khan, Fawwad Hassan Jaskani · 0 citations
Open access Sep 2026

SecurePrompt-IntegrityNet: Prompt-Injection-Resilient Data Integrity Verification for Agentic LLM Networks via Cryptographic Attestation and Activation Monitoring

Results demonstrate that jointly preserving cryptographic integrity symmetry and identifying activation-level asymmetry provides substantially stronger prompt-injection resilience than either verification mechanism alone.

Faisal Alhwikem, A. Khan, Fawwad Hassan Jaskani · 0 citations
Open access 2026

Teaching LLMs to Infer Real-World Consequences through Embodied Semantic Grounding

Embodied Semantic Grounding (ESG) is proposed, a framework that equips LLMs with consequence-aware text representations that extends affordance-grounding with consequence-level representations of post-event environmental functionality.

Manaswi Kulahara, Khadija Parwez, Faisal Alhwikem et al. · 0 citations
Aug 2026

Deep Evolutionary Software Design Using Adaptive Intelligence for Maintainability and Refactoring

Design-AI, a novel framework that integrates deep evolutionary algorithms with adaptive intelligence to automate the identification and recommendation of refactoring opportunities, achieves high precision and recall in recommendations, outperforming state-of-the-art academic and commercial refactoring solutions.

Faisal Alhwikem · 0 citations
Open access Jul 2026

A Mutation-Guided Safety Assurance Framework for Safety-Critical Cyber-Physical Systems

MuGu provides a rational basis for applying safety-oriented next-generation mutation testing in autonomous systems and determines 2.4 times as many safety-violating methods as state-of-the-art methods and decreases wasted effort on equivalent mutants by 64.2%.

Faisal Alhwikem · 0 citations

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