Synas Detect 1

Synas Detect 1 is a high-accuracy vision classifier (~304 million parameters) developed by Atugil Intelligence. Fine-tuned on modern synthetic media, it specializes in determining whether an image is an authentic photograph or AI-generated by models such as Midjourney, Stable Diffusion, DALL·E, Imagen, and similar generative systems.

Model Overview

Intended Use

Primary Use Cases

• Media Authenticity Verification – Detect synthetic imagery for journalism, publishing, and digital archives.
• Content Moderation – Flag AI-generated uploads within trust and safety pipelines.
• Forensic Visual Analysis – Assist researchers and investigators by identifying subtle generation artefacts.

Limitations

While Synas Detect 1 performs strongly across a wide variety of datasets, it has several limitations:

• Extremely compressed JPEG images or aggressive social media recompression may reduce confidence.
• Heavy cropping, resizing, or post-processing can remove useful forensic signals.
• The model is not explicitly trained against adversarial perturbations or deliberate evasion attacks.
• Classification confidence should not be treated as absolute proof of authenticity.

Training Setup

The model was fine-tuned on high-quality synthetic and real image pairs while preserving the strong visual representations learned by CLIP.

Performance

Performance varies depending on image quality, compression level, and the generation model used.

Usage

Transformers Pipeline

from transformers import pipeline
from PIL import Image

classifier = pipeline(
    "image-classification",
    model="Atugil-Intelligence/synas-detect-1"
)

image = Image.open("path_to_image.jpg").convert("RGB")

predictions = classifier(image)

print("Synas Detect 1 Predictions")
print("-" * 40)

for prediction in predictions:
    print(
        f"{prediction['label']} "
        f"({prediction['score'] * 100:.2f}%)"
    )

Native PyTorch Implementation

import torch
from PIL import Image
from transformers import (
    AutoImageProcessor,
    AutoModelForImageClassification,
)

MODEL_ID = "Atugil-Intelligence/synas-detect-1"

device = torch.device(
    "cuda" if torch.cuda.is_available() else "cpu"
)

processor = AutoImageProcessor.from_pretrained(MODEL_ID)
model = AutoModelForImageClassification.from_pretrained(
    MODEL_ID
).to(device)

model.eval()

image = Image.open("path_to_image.jpg").convert("RGB")

inputs = processor(
    image,
    return_tensors="pt"
).to(device)

with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=-1)
    prediction = probabilities.argmax(dim=-1).item()
    confidence = probabilities[0][prediction].item()

labels = {
    0: "AI Generated (Fake)",
    1: "Real Photograph",
}

print(f"Prediction : {labels[prediction]}")
print(f"Confidence : {confidence * 100:.2f}%")

License

Synas Detect 1 is released under the MIT License.

Attribution

This model is fine-tuned from OpenAI's CLIP ViT-Large Patch-14 vision backbone.

Copyright

• Copyright © 2021 OpenAI (Base Model)
• Copyright © 2026 Atugil Intelligence (Fine-Tuned Weights)

Citation

If you use Synas Detect 1 in research, benchmarks, or production systems, please cite:

@misc{synas_detect_1_2026,
  author       = {Atugil Intelligence},
  title        = {Synas Detect 1: High-Accuracy Synthetic Image Classifier},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Atugil-Intelligence/synas-detect-1}}
}