Detect deepfake videos with advanced frame-by-frame analysis. Our AI model identifies face swaps, lip sync manipulation, and AI-generated content in real time.
Upload a short video clip (max 30s) for frame-by-frame deepfake detection
Supports MP4, AVI, MOV, WebM
Overall Verdict
Flagged Frames
0%
Hover over frames to see detailed analysis
Face Swap Detected
0%
Lip Sync Issues
0%
Temporal Artifacts
0%
Our advanced AI model identifies multiple types of deepfake artifacts and manipulation techniques
Identifies face replacement and synthetic face generation in video frames
Detects audio-visual mismatches and lip sync inconsistencies
Catches AI-generated body movements and physical inconsistencies
Finds frame-to-frame artifacts and temporal distortions
Protect your news and entertainment content from deepfake manipulation
Verify video evidence and detect fabricated crime scene footage
Scale content moderation and prevent synthetic media spread
Video is decoded into individual frames using hardware-accelerated decoding. Our system extracts frames at configurable intervals (default 1 FPS) to balance accuracy and performance.
Each frame is analyzed independently by our neural network to detect synthetic artifacts, face manipulations, and generation patterns.
Cross-frame patterns are detected to identify temporal inconsistencies, unnatural transitions, and artifacts that span multiple frames.
Final confidence scores are computed by aggregating frame-level and temporal analysis results into a unified verdict.
Integrate video deepfake detection into your application with our simple API
import requests
import cv2
import base64
def analyze_video(video_path, api_key):
"""
Analyze a video for deepfakes using frame-by-frame detection
"""
cap = cv2.VideoCapture(video_path)
frame_results = []
frame_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
# Skip frames to reduce processing (adjust as needed)
if frame_count % 30 == 0: # Process 1 frame per second
# Encode frame to base64
_, buffer = cv2.imencode('.jpg', frame)
b64_frame = base64.b64encode(buffer).decode('utf-8')
# Send to detection API
response = requests.post(
'https://api.deepfakedetectionapi.ai/v1/detect',
headers={'Authorization': f'Bearer {api_key}'},
json={'image': b64_frame, 'model': 'video-frame'}
)
result = response.json()
frame_results.append({
'frame': frame_count,
'is_fake': result['is_fake'],
'confidence': result['confidence']
})
frame_count += 1
cap.release()
# Compute aggregate score
flagged = sum(1 for r in frame_results if r['is_fake'])
verdict = 'SUSPICIOUS' if flagged / len(frame_results) > 0.2 else 'AUTHENTIC'
return {
'verdict': verdict,
'flagged_percentage': (flagged / len(frame_results)) * 100,
'frame_results': frame_results
}
Start analyzing videos with frame-by-frame deepfake detection. Get API access and integrate in minutes.
No credit card required. 100 free analyses per month.