Neural Network Architecture

AI Image Detector uses a multi-stage deep learning pipeline built on an EfficientNet-B4 backbone fine-tuned specifically for the binary classification task of distinguishing real photographs from AI-generated images. The model was selected after extensive benchmarking against ResNet-50, ViT-Base, and ConvNeXt architectures, where it demonstrated the best balance of accuracy, inference speed, and generalization across unknown generators.

The feature extraction backbone feeds into a custom classification head with attention pooling, enabling the model to focus on regions of the image most indicative of synthetic generation — such as high-frequency texture areas, specular highlights, and fine structural details like hair, text, and reflections.

Training Data & Process

Our training dataset comprises over 4.2 million images balanced equally between authentic photographs and AI-generated outputs. The real image corpus is sourced from multiple licensed photographic datasets spanning diverse subjects, lighting conditions, cameras, and post-processing styles. The synthetic corpus includes outputs from over 30 distinct generative models including DALL-E 2, DALL-E 3, Midjourney (v4 through v6), Stable Diffusion (1.5, 2.1, XL, 3.0), Adobe Firefly, Google Imagen, and various open-source fine-tunes.

Training uses a progressive learning strategy: the model first learns basic real-vs-synthetic distinctions on lower-resolution crops, then fine-tunes on full-resolution images with advanced augmentation including JPEG compression simulation, social media re-encoding, screenshot artifacts, and aspect ratio variation. This progressive approach improves robustness to the quality degradation commonly found in images shared online.

Detection Signals

The model examines multiple families of detection signals simultaneously. Spectral analysis looks at the frequency decomposition of pixel values — real cameras introduce sensor noise with characteristic spectral properties that differ from the smoother noise patterns in AI outputs. Texture analysis evaluates micro-texture consistency, particularly in regions where AI generators commonly hallucinate (skin pores, fabric weave, foliage detail). Structural coherence checks identify physically implausible features such as asymmetric faces, impossible reflections, or inconsistent perspective geometry. Color channel analysis examines statistical correlations between RGB channels that differ measurably between camera-captured and AI-synthesized images.

Accuracy & Benchmarks

We evaluate our model on a held-out test set of 200,000 images not seen during training, refreshed quarterly with outputs from the latest generator releases. Current benchmark performance: 95.8% overall accuracy, 96.2% precision (low false positive rate on real images), 95.4% recall (high catch rate on AI images), and 0.987 AUC-ROC. Performance varies by generator — newer models like Midjourney v6 and DALL-E 3 are harder to detect than earlier generators, and heavily compressed or small images reduce accuracy across the board.

Continuous Improvement

AI image generation evolves rapidly, so our detection models must evolve with it. We retrain our production model on a monthly cadence incorporating outputs from newly released generators. Our research team monitors the generative AI landscape continuously, acquiring samples from new models within days of public release. Model updates are deployed through a blue-green deployment process with automated accuracy regression testing to ensure no degradation on previously detectable generators.

Limitations & Transparency

No AI detection system is infallible. Our model may produce less confident results on heavily edited photographs, screenshots of AI-generated images (which lose key frequency information), very low resolution images, or outputs from adversarially fine-tuned generators designed to evade detection. We report confidence scores rather than binary yes/no verdicts precisely because detection exists on a spectrum. We encourage users to treat our results as one signal among many when making high-stakes determinations about image authenticity.