abacusai/Smaug-AI-Image-Detector

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1. Model Introduction

Smaug-AI-Image-Detector sets a new state of the art for open-source AI-generated image detection. It is a fine-tune of Google's EmbeddingGemma-2 by Abacus.AI and the strongest open-source detector on real-world images.

A dedicated vision model, 41% smaller than its base. We streamlined EmbeddingGemma-2 into a vision-only architecture by removing its 305M-parameter audio tower: 439M parameters instead of 744M, with no loss in accuracy.

import sys
from huggingface_hub import snapshot_download
sys.path.insert(0, snapshot_download("abacusai/Smaug-AI-Image-Detector"))
from smaug_image_detect import SmaugImageDetect     # ships with this repo

detector = SmaugImageDetect.from_pretrained("abacusai/Smaug-AI-Image-Detector")
detector("photo.jpg")                                 # 0.97 -> probability the image is AI-generated

2. Highlights

• #1 on real-world images: 0.973 AUC on OpenFake Reddit; the next best open-source detector scores 0.947.
• Catches more at the same false-alarm rate: at 5% false alarms it catches 86.7% of AI images on Reddit; the next best catches 77.4%.
• #1 on OpenFake Core (0.991 AUC) and #1 on average across all three benchmarks (0.982 AUC).
• Precise: flags only 3.0% of real Reddit photos, 1.4% of real OpenFake photos and 0.4% of real CommunityForensics photos as AI.
• Current generators: GPT-Image 2, Nano Banana Pro, Midjourney 7, Seedream 5, FLUX.2, Z-Image, Recraft, Ideogram, ERNIE-Image, Sora 2 and Veo 3 frames, DALL·E 3, Firefly 3 and more.
• Runs anywhere: at 439M parameters it runs on a single consumer GPU (about 1 GB of memory), on Apple Silicon Macs via Metal, or on a plain CPU, so images can be checked locally without sending them anywhere.

3. Model Summary

4. Evaluation

Bold = best in the row. Shading is relative within each row (darker = better). All models scored on the same 26,070 test images. All open-source detectors tested

5. Training Approach

• Full fine-tune of EmbeddingGemma-2's image encoder with a linear classifier on its embedding.
• Native-resolution crops keep the pixel-level traces that generators leave, instead of shrinking the image.
• Robustness training with resizing, recompression and blur, so scores hold up on shared and re-saved copies.
• Hard negatives from professional photography, so a polished real photo is not mistaken for AI.

6. Limitations

• Brand-new generators, or ones built to evade detectors, may get through.
• A real photo with a small AI-edited region may be scored as real.
• A score is evidence, not proof: do not use it as the only basis for decisions about people or their work.

7. Usage

pip install "sentence-transformers[image]" pillow

from huggingface_hub import snapshot_download
import sys

path = snapshot_download("abacusai/Smaug-AI-Image-Detector")
sys.path.insert(0, path)
from smaug_image_detect import SmaugImageDetect

detector = SmaugImageDetect.from_pretrained(path)        # NVIDIA GPU, Apple Silicon (Metal) or CPU, picked automatically
detector("photo.jpg")                                     # -> float in [0, 1]
detector(["a.jpg", "b.png", pil_image], batch_size=16)   # -> list of floats
detector("photo.jpg", crops=1)                            # 3x faster

0.5 is a good default threshold; raise it when a false alarm is costly.

llama.cpp: use Smaug-AI-Image-Detector-GGUF. It includes the classification head and a client that applies the same crops; a GGUF of the image encoder alone returns an embedding, not a verdict.

8. License

Apache 2.0, the same license as the base model, EmbeddingGemma-2.

9. Citation

@misc{abacusai2026smaugaiimagedetector,
  title  = {Smaug-AI-Image-Detector},
  author = {Abacus.AI},
  year   = {2026},
  url    = {https://huggingface.co/abacusai/Smaug-AI-Image-Detector}
}