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4 papers

cs.LG2026

Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks

Hamid Dashtbani, Mehdi Dousti Gandomani, AmirMahdi Sadeghzadeh

The paper introduces Random Logit Scaling, a plug‑and‑play post‑processing defense that randomly rescales model logits to thwart black‑box score‑based adversarial attacks while kee…

cs.CV2026

Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models

Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh +1

Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model…

cs.CV2026

Erasure or Erosion? Evaluating Compositional Degradation in Unlearned Text-To-Image Diffusion Models

Arian Komaei Koma, Seyed Amir Kasaei, Ali Aghayari +2

Post-hoc unlearning has emerged as a practical mechanism for removing undesirable concepts from large text-to-image diffusion models. However, prior work primarily evaluates unlear…

cs.CL2025

EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models

Mohammad Reza Mirbagheri, Mohammad Mahdi Mirkamali, Zahra Motoshaker Arani +3

Large Language Models (LLMs), trained on extensive datasets using advanced deep learning architectures, have demonstrated remarkable performance across a wide range of language tas…