Deepfake Guardian
Identify-Threat &Kill
The problem Deepfake Guardian solves
The Problem: The Silent Violation
We are addressing a terrifying reality: Non-Consensual Deepfake Pornography.
For a victim, this is not a "prank" or a "tech demo." It is a digital assault.
- The Violation: A person—often a woman or a student—wakes up to find their face stolen and grafted onto an explicit video they never made. They haven't been touched physically, but the psychological violation is total.
- The Paralysis: The shame is immediate. Victims often suffer in silence because they are terrified that reporting it will only make more people watch it.
- The Burden of Proof: When they do try to report it, they face a humiliating interrogation: "Are you sure that isn't you?" "Prove it's fake."They are forced to re-watch their own abuse just to analyze it for a platform moderator who might not care.
The core problem is helplessness. The technology to ruin a reputation is instant and free, but the technology to save it is slow, expensive, and technical.
How We Are Solving It: A Privacy-First "Digital Shield"
We have built this platform specifically for victims of image-based abuse. We act as a forensic advocate that speaks the language of law and technology so the victim doesn't have to.
We solve this in three respectful, privacy-centric steps:
- The "Zero-Exposure" Vault (Safety First)
We know the last thing a victim wants to do is upload sensitive images to a server.
- Our Promise: We use a "Privacy-First" Architecture. When you upload the threat (the fake video) and your reference photos, our system processes them in a secure, temporary "vault."
- The Tech: We use Auto-Expiring Database Entries (TTL Indexes). Once the analysis is done, the raw images are permanently deleted. No human on our team ever sees them. You are safe here.
- The Biometric Alibi (Scientific Proof)
"It's not me" is an opinion. "The biometric landmarks do not match" is evidence.
- The Method: We analyze the fake video against your real "Identity Fingerprint" (from your normal photos).
- The Result: We generate a Report. This scientifically proves that while the face looks like you, the underlying biometric structure (ear shape, jaw movement, eye distance) belongs to someone else. This is your "Digital Alibi."
- The Legal "Kill Switch" (Automated Justice)**
The hardest part is going to the police or the platform and explaining what happened. We do that for you.
- The Action: Our system instantly generates a "Cyber-Cell Ready" Complaint Packet.
- The Content: It explicitly cites Section 66E (Violation of Privacy) and Section 67 (Obscenity) of the IT Act. It uses the correct legal terminology that forces platforms to take immediate action under "Safe Harbor" laws.
- The Outcome: You don't have to beg for removal; you demand it with a legal document in hand.
Our Mission: We turn a victim's shame into actionable power. We provide the tools to say: "This is a crime, here is the proof, and here is the law."
Challenges we ran into
- Dataset Availability
One of our first challenges was simply finding the right data. Real deepfake examples are hard to access because they’re sensitive, often private, and ethically restricted. On top of that, many public datasets don’t fully reflect real-world conditions like different lighting, image quality, or manipulation styles.
- Web Discovery / Scraping Logic
Another big challenge was figuring out where and how to look for deepfakes online. If you scan too much, you get overwhelmed with irrelevant data and legal risks. If you scan too little, you miss important cases. Striking that balance was a key technical and design challenge.
- Platform-Specific Content Handling
Every platform handles media differently—some focus on images, others on short videos, heavily compressed files, or thumbnails. Because of this, we couldn’t rely on a one-size-fits-all approach and had to think carefully about how content is processed across platforms.
- Model Generalization Across Sources
Even a strong deepfake detection model can struggle when content comes from different sources. Changes in resolution, compression, and format can affect accuracy, making it challenging to ensure reliable results across platforms.
Tracks Applied (1)
MongoDB Atlas
Major League Hacking
