AI System for Real-Time Scam Detection
Budget: ₹1,500 – ₹12,500 INR
Project Title: AI-Based "Digital Arrest" Scam Detection System (MVP)
Project Overview:
I am looking for an AI/ML developer to build a functional prototype of a security system designed to detect "Digital Arrest" scams. The system needs to analyze video and audio inputs in real-time (or near real-time) to identify deepfakes, threatening language, and fake law enforcement visuals.
Key Features Required (The Scope):
I need a desktop-based prototype (Python/Streamlit or similar) that can process a sample video feed or live webcam input and perform the following:
* Audio Threat Detection (NLP):
* Transcribe audio in real-time (using OpenAI Whisper or Google Speech-to-Text).
* Detect specific scam keywords/intents (e.g., "money laundering," "CBI," "narcotics," "arrest," "isolate yourself").
* Flag high-pressure/threatening tones.
* Visual Forensics (Computer Vision):
* Liveness/Deepfake Detection: Identify if the face in the video is AI-generated (looking for lack of blinking, lip-sync errors, or artifacts).
* Uniform/Badge Recognition: Detect if the person is wearing a police uniform or showing a badge (using object detection like YOLO).
* Real-Time Risk Dashboard:
* A simple UI that displays a "Trust Score." If the score drops below a threshold, it shows a "SCAM ALERT" warning.
Preferred Tech Stack:
* Language: Python
* ML Frameworks: TensorFlow / PyTorch / Keras
* Computer Vision: OpenCV, MediaPipe
* NLP: Hugging Face Transformers (BERT/RoBERTa for intent classification)
* Interface: Streamlit or Flask (for the demo dashboard)
Deliverables:
* Source Code (well-commented).
* A requirements.txt file for easy installation.
* A short demo video showing the system detecting a scam attempt from a sample video file.
* Documentation on the model architecture used.
Screening Questions
Questions for you?
* "For the deepfake detection, will you be training a model from scratch, or do you plan to use a pre-trained model like XceptionNet or MesoNet? Why?" (A good dev will suggest pre-trained models to save time/cost).
* "How will you handle the latency? If we use Whisper for audio transcription, will it be fast enough for a live alert?"
* "Do you have experience with 'Multimodal' analysis (combining audio and video data), or will these run as separate independent modules?"
Option A: The Screen-Reflection Test
Implement a feature where the screen flashes a random color sequence.
Build a CV model that attempts to detect this color change in the reflection of the caller's eyes/glasses.
Goal: Prove the caller is a live feed and not a deepfake/loop.
Option B: Environmental Consistency Check
Build a classifier that labels the "Visual Scene" (e.g., Office, Outdoors, Car) and the "Audio Scene" (e.g., Echoey, Windy, Traffic).
Trigger an alert if they do not match (e.g., Visual = Office, Audio = Traffic/Wind).
Project Overview:
I am looking for an AI/ML developer to build a functional prototype of a security system designed to detect "Digital Arrest" scams. The system needs to analyze video and audio inputs in real-time (or near real-time) to identify deepfakes, threatening language, and fake law enforcement visuals.
Key Features Required (The Scope):
I need a desktop-based prototype (Python/Streamlit or similar) that can process a sample video feed or live webcam input and perform the following:
* Audio Threat Detection (NLP):
* Transcribe audio in real-time (using OpenAI Whisper or Google Speech-to-Text).
* Detect specific scam keywords/intents (e.g., "money laundering," "CBI," "narcotics," "arrest," "isolate yourself").
* Flag high-pressure/threatening tones.
* Visual Forensics (Computer Vision):
* Liveness/Deepfake Detection: Identify if the face in the video is AI-generated (looking for lack of blinking, lip-sync errors, or artifacts).
* Uniform/Badge Recognition: Detect if the person is wearing a police uniform or showing a badge (using object detection like YOLO).
* Real-Time Risk Dashboard:
* A simple UI that displays a "Trust Score." If the score drops below a threshold, it shows a "SCAM ALERT" warning.
Preferred Tech Stack:
* Language: Python
* ML Frameworks: TensorFlow / PyTorch / Keras
* Computer Vision: OpenCV, MediaPipe
* NLP: Hugging Face Transformers (BERT/RoBERTa for intent classification)
* Interface: Streamlit or Flask (for the demo dashboard)
Deliverables:
* Source Code (well-commented).
* A requirements.txt file for easy installation.
* A short demo video showing the system detecting a scam attempt from a sample video file.
* Documentation on the model architecture used.
Screening Questions
Questions for you?
* "For the deepfake detection, will you be training a model from scratch, or do you plan to use a pre-trained model like XceptionNet or MesoNet? Why?" (A good dev will suggest pre-trained models to save time/cost).
* "How will you handle the latency? If we use Whisper for audio transcription, will it be fast enough for a live alert?"
* "Do you have experience with 'Multimodal' analysis (combining audio and video data), or will these run as separate independent modules?"
Option A: The Screen-Reflection Test
Implement a feature where the screen flashes a random color sequence.
Build a CV model that attempts to detect this color change in the reflection of the caller's eyes/glasses.
Goal: Prove the caller is a live feed and not a deepfake/loop.
Option B: Environmental Consistency Check
Build a classifier that labels the "Visual Scene" (e.g., Office, Outdoors, Car) and the "Audio Scene" (e.g., Echoey, Windy, Traffic).
Trigger an alert if they do not match (e.g., Visual = Office, Audio = Traffic/Wind).