MECP-GAP: Mobility-Aware MEC Planning via GNNs
A backdoor behaves like a step function: invisible until its trigger fires, fully present the instant it does. That makes it a bad target for anything that relies on a smooth gradient to search for it. This project proposes a way to temporarily turn that step into a slope, so the hidden behavior can be read out directly instead of searched for blindly.
The core idea is to identify the specific direction in a fine-tuned model’s weights that the fine-tuning process itself added, then exaggerate that direction rather than perturbing the model randomly. Amplifying a targeted direction should reveal the hidden behavior far more efficiently than random weight noise, since essentially all of the perturbation budget is spent along the one direction that matters instead of being spread thinly across the whole model. The amplification is then gradually relaxed back toward the original model while tracking which inputs still trigger the behavior, narrowing in on the actual trigger.
This is currently a research design with a clear, testable first prediction — that targeted amplification should outperform undirected weight perturbation by a wide margin at equal collateral damage — rather than a validated detector. That comparison is the next experiment before any detection claims are made.
Overview Most simple backdoor checks look for a specific trigger phrase. That breaks the moment the trigger is unknown or paraphrased. This project asks a di...
The Sentinel’s Dilemma: Who Guards Our AI Guardians?
Overview A backdoor behaves like a step function: invisible until its trigger fires, fully present the instant it does. That makes it a bad target for anythi...
Overview A model that gets flagged as “probably backdoored” is not the same as a model whose exact trigger and payload have been proven, causally, to be conn...
Overview A widely cited graph neural network for detecting vulnerable code is reproduced from scratch on the authors’ own released data, then put under three...
Overview Existing LLM backdoor scanners mostly search for an object: a trigger string, a target string, or a memorized leaked example. This project explores ...
Overview A backdoor scanner that only gets tested against easy, textbook backdoors will look better than it is. This project builds a “weakness zoo” — a deli...
Overview Dental panoramic X-rays are read manually by clinicians, and there is growing interest in whether general-purpose medical vision-language models can...
Overview Most backdoor-detection papers evaluate on large models behind a GPU cluster, which makes it hard to build intuition about when a detection signal a...
Unveiling BackdoorBench: A Critical Benchmark for AI Security
Overview In this project, we fine-tuned the Wav2Vec2 model to perform sentiment analysis based on both voice features and text transcripts from the Shemo da...
Overview In this project, we developed an Android application to estimate the location of cellular network cells using Received Signal Strength Indicator (RS...
Overview In this project, we explore multimodal sentiment analysis, which involves analyzing both text and image data together. Our goal is to predict sentim...
Project Iridium
My Internship at the NLP Lab :
Its My Heroku project :
My sonic pi project : . این پست من مربوط به پوروژه سونیک پای بنده است
My works and wishes Success secret مصاحبه با جناب اقا پارسا: ایشون بسیار ادم سخت کوشی بودن و گفتن که حتی در دوران دانشجویی شون درس هم میدادن 1 به موار...
Its my favorite university This is oxford: In England The best unniversity. this university is first inthe world. this university have best descover...
Its My Hackathon project :