MECP-GAP: Mobility-Aware MEC Planning via GNNs
Most simple backdoor checks look for a specific trigger phrase. That breaks the moment the trigger is unknown or paraphrased. This project asks a different question: can a small “watchdog” model learn to recognize a backdoor from how a suspect model behaves, rather than from what exact words it responds to?
The project has two phases. First, acting as the attacker, a base model is fine-tuned with both backdoored and clean LoRA adapters to build a labeled set of examples. Second, acting as the defender, a small distilled model is trained on statistical signals extracted from those adapters — how confident the suspect model is about its own outputs, and how semantically consistent its response is with the prompt. A multi-head architecture lets the watchdog separately flag suspicious input tokens, suspicious output tokens, and issue an overall verdict.
The pipeline produces a working end-to-end classifier: given a new LoRA adapter, it outputs a CLEAN, SUSPICIOUS, or BACKDOOR DETECTED verdict without needing to know the trigger or target in advance. The main takeaway is that confidence and semantic-consistency signals carry real information about backdoor behavior even when the exact trigger text is never given to the detector.
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 :
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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 :