29 citations · 75 across the 15 of their papers we have counts for
17 papers
Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models
Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia +1
Large language models (LLMs) increasingly serve users whose expectations are shaped by their cultural context, yet most cultural evaluations test what a model knows rather than how…
Fanar 2.0: Arabic Generative AI Stack
FANAR TEAM, Ummar Abbas, Mohammad Shahmeer Ahmad +34
We present Fanar 2.0, the second generation of Qatar's Arabic-centric Generative AI platform. Sovereignty is a first-class design principle: every component, from data pipelines to…
Do I Really Know? Learning Factual Self-Verification for Hallucination Reduction
Enes Altinisik, Masoomali Fatehkia, Fatih Deniz +4
Factual hallucination remains a central challenge for large language models (LLMs). Existing mitigation approaches primarily rely on either external post-hoc verification or mappin…
FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models
Masoomali Fatehkia, Enes Altinisik, Husrev Taha Sencar
Content moderation filters are a critical safeguard against alignment failures in language models. Yet most existing filters focus narrowly on general safety and overlook cultural…
Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
Asim Ersoy, Enes Altinisik, Husrev Taha Sencar +1
Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resou…
PAM: Training Policy-Aligned Moderation Filters at Scale
Masoomali Fatehkia, Enes Altinisik, Mohamed Osman +1
Large language models (LLMs) remain vulnerable to misalignment and jailbreaks, making external safeguards like moderation filters essential, yet existing filters often focus narrow…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…
Explaining the role of Intrinsic Dimensionality in Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar +2
Adversarial Training (AT) impacts different architectures in distinct ways: vision models gain robustness but face reduced generalization, encoder-based models exhibit limited robu…
ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search
Enes Altinisik, Fatih Deniz, Husrev Taha Sencar
We present ProvG-Searcher, a novel approach for detecting known APT behaviors within system security logs. Our approach leverages provenance graphs, a comprehensive graph represent…
A3T: Accuracy Aware Adversarial Training
Enes Altinisik, Safa Messaoud, Husrev Taha Sencar +1
Adversarial training has been empirically shown to be more prone to overfitting than standard training. The exact underlying reasons still need to be fully understood. In this pape…
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar +2
Adversarial training is widely acknowledged as the most effective defense against adversarial attacks. However, it is also well established that achieving both robustness and gener…
Exploration of Enterprise Server Data to Assess Ease of Modeling System Behavior
Enes Altinisik, Husrev Taha Sencar, Mohamed Nabeel +2
Enterprise networks are one of the major targets for cyber attacks due to the vast amount of sensitive and valuable data they contain. A common approach to detecting attacks in the…
Video Source Characterization Using Encoding and Encapsulation Characteristics
Enes Altinisik, Husrev Taha Sencar, Diram Tabaa
We introduce a new method for camera-model identification. Our approach combines two independent aspects of video file generation corresponding to video coding and media data encap…
Automatic Generation of H.264 Parameter Sets to Recover Video File Fragments
Enes Altinisik, Hüsrev Taha Sencar
We address the problem of decoding video file fragments when the necessary encoding parameters are missing. With this objective, we propose a method that automatically generates H.…
PRNU Estimation from Encoded Videos Using Block-Based Weighting
Enes Altinisik, Kasim Tasdemir, Husrev Taha Sencar
Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera im…
Source Camera Verification from Strongly Stabilized Videos
Enes Altinisik, Husrev Taha Sencar
Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution fr…
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
Enes Altınışık, Kasım Taşdemir, Hüsrev Taha Sencar
The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures.…