This paper benchmarks the performance of OpenAI's Whisper model on diverse Arabic speech recognition tasks, using publicly available data and novel dialect evaluation sets. The study explores zero-shot, few-shot, and full finetuning scenarios. Results indicate that while Whisper outperforms XLS-R models in zero-shot settings on standard datasets, its performance drops significantly when applied to unseen Arabic dialects.
The paper introduces VENOM, a text-driven framework for generating high-quality unrestricted adversarial examples using diffusion models. VENOM unifies image content generation and adversarial synthesis into a single reverse diffusion process, enhancing both attack success rate and image quality. The framework incorporates an adaptive adversarial guidance strategy with momentum to ensure the generated adversarial examples align with the distribution of natural images.
Dr. Abdelrahman AlMahmoud from TII's Secure Systems Research Center (SSRC) will participate in a WGISTA webinar on adopting a digital mindset in auditing and fighting corruption. The webinar, organized by the International Organization of Supreme Audit Institutions (INTOSAI), will discuss the impact of emerging technologies on public sector auditing. Dr. AlMahmoud will share insights on how AI and Big Data can enable auditors to process data at a new scale. Why it matters: This highlights the UAE's growing role in applying advanced technologies like AI and big data to improve governance and accountability in the public sector.