UniversalFakeDetect ResNet-50 model analyzes the image
03
Get Verdict
Receive instant authenticity result with confidence score
94%
탐지 정확도
2-5s
속도
720K+
훈련 이미지
Free
무료
// About UniversalFakeDetect
UniversalFakeDetect is a state-of-the-art deepfake detection model built on ResNet-50 architecture,
trained on 720,000+ images (half real, half AI-generated). It achieves 93-95% accuracy across
various
AI generators including DALL-E, Midjourney, Stable Diffusion, and GAN-based face swaps.
The model uses advanced data augmentation including Gaussian blur and JPEG compression during
training,
making it robust against common image transformations. All processing runs on-server - your images
are
analyzed securely and deleted after processing.
// Frequently Asked Questions
A deepfake detector is an AI-powered tool that analyzes images to determine if they've been
artificially generated or manipulated. It uses neural networks trained on millions of real and
fake images to identify subtle artifacts, inconsistencies, and patterns that distinguish
authentic photos from AI-generated content.
Our multi-method ensemble achieves 90-95% accuracy across various AI generators. Individual
methods like CNNDetection (ResNet-50) achieve 93-95% accuracy, while combining multiple
approaches reduces false positives and improves reliability.
Yes! Our detector is specifically trained to identify GAN-based face swaps. It analyzes facial
boundary artifacts, blending inconsistencies, and noise patterns that occur when faces are
swapped using tools like DeepFaceLab or FaceSwap.
Error Level Analysis (ELA) detects compression inconsistencies by re-saving the image and
comparing error levels. Neural network detection uses deep learning to recognize patterns from
training data. We use both methods for comprehensive analysis - ELA is good for edited regions,
while neural networks excel at detecting AI-generated content.
Yes! We use open-source models including CNNDetection (ResNet-50) and UniversalFakeDetect. These
models are available on GitHub and trained on public datasets like FaceForensics++ and the Wang
et al. CNN-generated images dataset.
Convolutional Neural Networks (CNNs) learn hierarchical features from images. Lower layers
detect edges and textures, while deeper layers recognize complex patterns. For deepfake
detection, CNNs are trained to distinguish authentic camera noise, lighting, and compression
patterns from the synthetic artifacts left by AI generators.
While adversarial attacks can potentially fool single-method detectors, our multi-method
ensemble approach provides resilience. Different detection methods analyze different aspects of
the image, making it much harder for an adversary to evade all detection mechanisms
simultaneously.
Our models are trained on datasets including FaceForensics++, DFDC (DeepFake Detection
Challenge), Celeb-DF, and custom datasets with images from DALL-E, Midjourney, Stable Diffusion,
and various GAN architectures. Training data includes 720,000+ images (half real, half
AI-generated).
AI generators often leave periodic artifacts in the frequency domain. Using FFT (Fast Fourier
Transform) and DCT analysis, we detect checkerboard patterns from transposed convolutions,
upsampling artifacts, and unnatural spectral distributions that are invisible to the human eye.
Our detection typically takes 2-5 seconds per image. While not suitable for real-time video
streaming, it's fast enough for most verification use cases. Real-time detection for video
requires specialized hardware acceleration and optimized models.
Currently, our tool focuses on image detection. Audio deepfake detection requires different
techniques like spectral analysis and voice pattern recognition. We're exploring multimodal
detection capabilities for future updates.
Liveness detection verifies if a face belongs to a living person present at capture time (used
in identity verification). Deepfake detection determines if an image was AI-generated or
manipulated. They're complementary - liveness checks prevent spoofing, while deepfake detection
identifies synthetic content.
ELA re-saves the image at a known compression level and compares it to the original. Areas that
have been edited or artificially inserted show different error levels than original content,
appearing as bright spots in the ELA visualization. This reveals splicing, cloning, and
AI-generated regions.
We currently offer a free web interface for individual use. For API access and bulk processing
capabilities, please contact us. We're developing an API solution for enterprise customers who
need to integrate deepfake detection into their platforms.
Deepfakes raise concerns about misinformation, identity theft, non-consensual content, election
manipulation, and erosion of trust in visual media. Detection tools like ours help combat these
threats by empowering people to verify authenticity of content they encounter online.
Yes! Deepfake detection is increasingly used in KYC (Know Your Customer) processes, insurance
claims verification, social media moderation, and journalism fact-checking. It helps identify
synthetic identity documents, fake profile photos, and AI-generated evidence.
Detector performance is measured using accuracy, precision, recall, and AUC-ROC on standard
datasets. Key benchmarks include FaceForensics++, DFDC, and Celeb-DF. Cross-generator
generalization (detecting unseen AI models) is also important to evaluate.
Multimodal detection analyzes multiple signal types (video, audio, text) together. For example,
detecting lip-sync inconsistencies between audio and video, or verifying that voice and face
biometrics match. This provides stronger detection than analyzing each modality separately.
Our models are trained with data augmentation (blur, compression, resizing) to handle real-world
image transformations. However, new AI generators may evade detection initially. We continuously
retrain our models on emerging deepfake technologies to maintain robustness.
No detector can guarantee 100% authenticity. Our analysis provides probability scores based on
multiple detection methods. For legal or critical decisions, we recommend combining AI detection
with metadata verification, provenance tracking (C2PA), and contextual investigation.