MECP-GAP: Mobility-Aware MEC Planning via GNNs
A backdoor scanner that only gets tested against easy, textbook backdoors will look better than it is. This project builds a “weakness zoo” — a deliberately varied collection of backdoored language model adapters, including harder variants designed to probe the edges of what a leading scanning method can detect — to find where an existing state-of-the-art scanner actually breaks.
Standard backdoors are planted the conventional way: a fixed trigger paired with a fixed target response, injected via LoRA fine-tuning. Alongside these, harder variants are trained where the “target” is not a single fixed string but a family of semantically related paraphrases, diluting the signal the scanner relies on. Every model in the zoo — easy and hard — is then scanned with the same detector and measured on whether the backdoor was flagged, whether it still fires, and whether normal behavior on clean input is preserved.
The scanner reliably caught the standard, single-target backdoors, confirming it works as intended on the case it was designed for. It missed the harder, semantically diluted variants — exactly the gap the zoo was built to expose. That contrast is the main result: it locates a concrete, reproducible blind spot in a widely used detection method rather than a general claim that the method doesn’t work.
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
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