How RD Detects Image Deepfakes
Reality Defender uses advanced deep learning to detect images created or manipulated by AI systems. Our models are trained on a large in-house dataset of both real and fake images — including millions of examples generated by GANs and diffusion-based architectures.
Detection Methodology
We use state-of-the-art visual transformer models (ViTs) and other neural architectures that excel at identifying synthetic texture, lighting, and structural patterns in images.
Our models are trained on simulated real-world conditions — we compress, crop, and resize training images to make the detectors robust to platform artifacts like social media recompression.
Each image is assigned a confidence score (1–99%), representing the likelihood of AI generation or manipulation.
This approach ensures that detections remain reliable even when images are downsampled, filtered, or slightly altered.
How Image Deepfakes Are Created
Bad actors can synthesize or manipulate images in multiple ways:
Full-face generation: Systems like StyleGAN2/3 or Stable Diffusion create fully synthetic faces or portraits that never existed.
Partial face generation / swapping: A source face is swapped onto a target image using GAN-based blending and inpainting to remove visual seams.
Inpainting and compositing: Generative models fill in missing or masked image regions, often used for subtle manipulations rather than full synthesis.
These techniques produce increasingly realistic results, but each leaves subtle statistical traces that RD’s detectors are trained to identify.
At Reality Defender, generalization, the ability for models to perform well on unseen examples, is a key principle. We aim to ensure detectors can adapt to novel generative methods and real-world artifacts over time.
Related FAQs
Question | Answer |
How do you detect image-based deepfakes? | Using transformer-based neural networks trained on compressed, real-world data. |
Which generative models are covered? | StyleGAN family, BigGAN, Stable Diffusion, Midjourney, DALL·E, and others. |
What affects accuracy? | Compression, cropping, resizing, blur, and domain novelty. |
Why might a result be wrong? | Image manipulations may hide telltale artifacts or fall outside the training distribution. |
How often do you update the models? | Continuously, to stay ahead of emerging generative methods and adversarial attacks. |