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Menampilkan 1–5 dari 5 artikel
AI-Driven Disinformation Campaigns: Detecting Synthetic Propaganda in Encrypted Messaging via Graph Neural Networks
Anil Kumar Pakina
; Ashwin Sharma
; Deepak Kejriwal
International Journal Science and Technology (IJST)
Vol 4
, No 1
(2025)
The rapid rise of generative AI has fueled more sophisticated disinformation campaigns, particularly on encrypted messaging platforms like WhatsApp, Signal, and Telegram. While these platforms protect user privacy through end-to-end encryption, they pose significant challenges to traditional content moderation. Adversaries exploit this privacy to disseminate undetectable synthetic propaganda, influencing public opinion and destabilizing democratic processes without leaving a trace.
This research...
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Adversarial AI in Social Engineering Attacks: Large- Scale Detection and Automated Counter measures
Anil Kumar Pakina
; Deepak Kejriwal
; Tejaskumar Dattatray Pujari
International Journal Science and Technology (IJST)
Vol 4
, No 1
(2025)
Social engineering attacks using AI-generated deepfake information leverage rare cybersecurity threat hunting. Conventional phishing detection and fraud prevention systems are failing to catch detection errors due to AI-generated social engineering in email, voice, and video content. To mitigate the increased risk of AI-driven social engineering attacks, a new multi-modal AI defense framework, incorporating Transfer Learning through pre-trained language models, deep fake sound analysis, and beha...
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Efficient TinyML Architectures for On-Device Small Language Models: Privacy-Preserving Inference at the Edge
Mangesh Pujari
; Anshul Goel
; Anil Kumar Pakina
International Journal Science and Technology (IJST)
Vol 3
, No 3
(2024)
Deploying small language models (SLMs) on ultra-low-power edge devices requires careful optimization to meet strict memory, latency, and energy constraints while preserving privacy. This paper presents a systematic approach to adapting SLMs for Tiny ML, focusing on model compression, hardware-aware quantization, and lightweight privacy mechanisms. We introduce a sparse ternary quantization technique that reduces model size by 5.8× with minimal accuracy loss and an efficient federated fine-tuning...
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Neuro- Symbolic Compliance Architectures: Real-Time Detection of Evolving Financial Crimes Using Hybrid AI
Anil Kumar Pakina
; Mangesh Pujari
International Journal Science and Technology (IJST)
Vol 3
, No 3
(2024)
This paper proposes NeuroSym-AML, a new neuro-symbolic AI framework explicitly designed for the real-time detection of evolving financial crimes with a special focus on cross-border transactions. By combining Graph Neural Networks (GNNs) with interpretable rule-based reasoning, our system dynamically adapts to emerging money laundering patterns while ensuring strict compliance with FATF/OFAC regulations. In contrast to static rule-based systems, NeuroSym-AML shows better performance-an 83....
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Balancing Innovation and Privacy: A Red Teaming Approach to Evaluating Phone-Based Large Language Models under AI Privacy Regulations
Mangesh Pujari
; Anil Kumar Pakina
; Anshul Goel
International Journal Science and Technology (IJST)
Vol 2
, No 3
(2023)
The rapid deployment of large language models (LLMs) on mobile devices has introduced significant privacy concerns, particularly regarding data collection, user profiling, and compliance with evolving AI regulations such as the GDPR and the AI Act. While these on-device LLMs promise improved latency and user experience, their potential to inadvertently leak sensitive information remains understudied. This paper proposes a red teaming framework to systematically assess the privacy risks of phone-...
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