AwaitSol
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AI / Media & SecurityFakeXpose

Deepfake Detection with Deep Learning

Developed an AI platform that analyzes audio and video to determine whether media is real or manipulated, with real-time verification, explainability, and confidence scoring.

Deepfake Detection with Deep Learning

Overview

AI-generated audio and video have become cheap and convincing enough to impersonate executives, public figures, and ordinary people. For journalists, brands, and anyone whose reputation depends on what's real, the question 'is this genuine?' now needs a fast, credible answer.

FakeXpose set out to provide that answer in a single click. AwaitSol built the detection platform: deep-learning models that analyze audio and video for signs of manipulation, and the web application and APIs that deliver verdicts users can understand and trust.

The Challenge

Deepfake detection is an adversarial problem. Generation techniques improve constantly, artifacts that give away one generation method disappear in the next, and manipulation can live in the audio track, the visual track, or only in the mismatch between them.

A bare 'fake' or 'real' label isn't enough either. Users need to know how confident the system is and why it reached its verdict — otherwise a false positive can damage trust as much as a missed fake. And verification has to be fast enough to use while content is still spreading.

Our Approach

1

We built separate deep-learning pipelines in PyTorch and TensorFlow for visual and audio analysis, with the results combined into a single verdict so manipulation in either channel — or inconsistency between them — is caught.

2

Every verdict ships with a confidence score and an explanation of the signals that drove it, so users can weigh the result rather than simply accept it. Models were validated against held-out media to measure accuracy before release.

3

The models are served behind FastAPI endpoints with MongoDB for results storage, powering a simple upload-and-verify web experience and making the same detection available programmatically.

What We Built

FakeXpose gives anyone a one-click way to check whether audio or video has been manipulated — with a real-time verdict, a confidence score, and an explanation of why.

  • Deep-learning models for video manipulation detection
  • Audio deepfake detection pipeline
  • Confidence scoring and explainability layer
  • FastAPI inference services
  • Upload-and-verify web application

Key Results

Real-time verification of uploaded audio and video
Every verdict explained, with a confidence score
Detection available through the web app and APIs

Users no longer have to guess whether a clip is genuine. FakeXpose delivers a fast, explainable verdict they can act on — and defend — before manipulated media does its damage.

Technology

PythonPyTorchTensorFlowFastAPIMongoDBDeep Learning
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