Project Info
This project did not submit a demo video on Devpost.
Inspiration
My mother sent me a post that was AI generated, she fully believed it and it was difficult to convince her of the truth, AI has gotten so intelligent that it is fooling the older and non technical folks of our society at an unprecedented level. Furthermore, there are nefarious webpages dedicated to deepfakes of real women in explicit poses, without their consent.
What it does
This is a google extension that allows a person to manually detect if an image, video, audio, or text is AI generated. The magnum opus is that it is able to run in real time and remove altered and nefarious AI posts from your YouTube, Twitter (X), Instagram, Tiktok etc. feeds, removing the worry that something is altered and protecting people like my mother who could also get potentially misinformed, scammed, or worse.
How we built it
We (I) built it using multiple LLM's for guidance and ran huggingface models locally that have 100+M parameters alongside LLM's (Llama via Groq) dedicated to detecting deepfake faces, deepfake audio, deepfake texts, and deepfake videos. We used fastapi as our backend along with Javascript and google web extensions to build the extension.
Challenges we ran into
It was very difficult to find and optimize a model so that real time detection does not take minutes to clear a feed, along with removing youtube posts, twitter posts, instagram comments, etc. It was also difficult to get fast llm readings (until groq!). Furthermore, using a CPU only build without CUDA made processing difficult.
Accomplishments we're proud of
We are proud that we were able to accomplish real time detection and were able to scan and remove posts quickly for the user.
What we learned
Learned a lot about deepfakes, a lot of huggingface and their models, along with trying Groq for the first time. This was also the first time making an extension.
What's next
Next up is optimizing and going beyond an extension to hopefully detect explicit material of unconsented individuals and reporting it to authorities, we also want to improve on detection and confidence with finetuning (veo 3 is difficult to detect.) Along with utilizing our detection technology to prevent scammers and impersonators. But before all of that we need to deploy a server. We also want to improve our UI as it is very barebones and basically auto generated, we spent a lot of our time on the backend.
Analysis
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Metric
- 14
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- AnthropicIn code
- CSSIn code
- FastAPIIn code
- HTMLIn code
- Hugging FaceIn code
- JavaScriptIn code
- PythonIn code
- PyTorchIn code
8 of 8 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
259 KB
Source files
44
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
ohsmaan/ai-deepfake-detection
59 files · 477 KB · @ a53ca98
Structure
Interface
4 files · 7%Screens, components and styles rendered to the user.
Application logic
31 files · 53%Domain rules, services and shared utilities.
+1 more
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- JavaScript45%
- Python43%
- HTML4%
- CSS4%
- Markdown4%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
pyproject.toml
pypi · 25- aiofiles
- anthropic
- fastapi
- groq
- huggingface-hub
- numpy
- opencv-python
- pillow
- pydub
- python-dotenv
- python-multipart
- requests
- scikit-learn
- scipy
- sentence-transformers
- soundfile
- torch
- torchaudio
- +7 more
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
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