4 citations · 7 across the 14 of their papers we have counts for
16 papers
Embeddings of Generalized Weighted Morrey Spaces
Zhen Liu
We study continuous embeddings from two classes of generalized weighted Morrey spaces into generalized Morrey spaces. The Komori--Shirai-type spaces use the weighted measure of cub…
The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
Toluwani Aremu, Manit Baser, Mohan Gurusamy +2
Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards. Recent work has…
ReACT-CLIP: Response-Aware Test-Time Defense for Vision--Language Models
Hashmat Shadab Malik, Toluwani Aremu, Samuele Poppi +2
Training-free test-time defenses offer a practical way to improve the adversarial robustness of CLIP-style vision--language models without modifying the pretrained model. However,…
WaxalNet: Benchmarking and Adapting Speech Recognition for Spontaneous Speech in 19 African Languages
Victor Tolulope Olufemi, Oreoluwa Babatunde, Ramsey Njema +28
Multilingual speech foundation models claim broad language coverage, but how well they handle spontaneous African speech, and how best to adapt them, remains unclear. We benchmark…
Watermarking Should Be Treated as a Monitoring Primitive
Toluwani Aremu, Nils Lukas, Jie Zhang
Watermarking is a primary means for providers of generative models to meet legal requirements to mark synthetic content. A watermark embeds a hidden signal into generated content t…
Aligning Stuttered-Speech Research with End-User Needs: Scoping Review, Survey, and Guidelines
Hawau Olamide Toyin, Mutiah Apampa, Toluwani Aremu +6
Atypical speech is receiving greater attention in speech technology research, but much of this work unfolds with limited interdisciplinary dialogue. For stuttered speech in particu…
Adaptively Robust LLM Monitoring via Activation Watermarking
Toluwani Aremu, Daniil Ognev, Samuele Poppi +1
Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent. LLM monitoring is deterministic and often openly available, so …
Mitigating Watermark Forgery in Generative Models via Randomized Key Selection
Toluwani Aremu, Noor Hussein, Munachiso Nwadike +5
Watermarking enables GenAI providers to verify whether content was generated by their models. A watermark is a hidden signal in the content, whose presence can be detected using a…
RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles
Munachiso Nwadike, Zangir Iklassov, Toluwani Aremu +6
We introduce the concept of the self-referencing causal cycle (abbreviated RECALL) - a mechanism that enables large language models (LLMs) to bypass the limitations of unidirection…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts
Toluwani Aremu, Oluwakemi Akinwehinmi, Chukwuemeka Nwagu +4
We investigate and observe the behaviour and performance of Large Language Model (LLM)-backed chatbots in addressing misinformed prompts and questions with demographic information…
Optimizing Adaptive Attacks against Watermarks for Language Models
Abdulrahman Diaa, Toluwani Aremu, Nils Lukas
Large Language Models (LLMs) can be misused to spread unwanted content at scale. Content watermarking deters misuse by hiding messages in content, enabling its detection using a se…
MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
Samar Fares, Klea Ziu, Toluwani Aremu +5
Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically designed to bypass existing defenses. To…
Redefining Contributions: Shapley-Driven Federated Learning
Nurbek Tastan, Samar Fares, Toluwani Aremu +2
Federated learning (FL) has emerged as a pivotal approach in machine learning, enabling multiple participants to collaboratively train a global model without sharing raw data. Whil…
Regularized PolyKervNets: Optimizing Expressiveness and Efficiency for Private Inference in Deep Neural Networks
Toluwani Aremu
Private computation of nonlinear functions, such as Rectified Linear Units (ReLUs) and max-pooling operations, in deep neural networks (DNNs) poses significant challenges in terms…
SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence
Toluwani Aremu, Li Zhiyuan, Reem Alameeri +2
Detection of violence and weaponized violence in closed-circuit television (CCTV) footage requires a comprehensive approach. In this work, we introduce the \emph{Smart-City CCTV Vi…