SSUPER-AIGID
On-device AI-generated image detector — 🥈 2nd place, LPCVC 2026 Track 3 (IEEE Low-Power Computer Vision Challenge, ECV Workshop @ CVPR 2026).
A vision-language model that not only classifies whether an image is AI-generated, but also explains its reasoning across 8 forensic criteria (lighting, edges, texture, perspective, physical plausibility, text, human elements, material detail). Designed to run fully on-device on the Snapdragon® 8 Elite NPU.
- 📦 This repository hosts the deployable QNN binary package (
SSUPER-AIGID.zip, ~2.6 GB). - 💻 Training / quantization code: GITHUB:LPCV-SSUPER-POWER
Results
| Metric | Value |
|---|---|
| Challenge score | 0.72 |
| Throughput | 31.21 tokens/sec (≈2× the requirement) |
| Placement | 🥈 2nd place |
| Target hardware | Snapdragon 8 Elite QRD (QNN) |
| Package size | ~2.62 GB |
Approach
A 3-stage pipeline:
- Annotation — Qwen2.5-VL auto-labels ~788K images with domain tags and per-criterion forensic scores (8 criteria).
- Training — Qwen2-VL-2B fine-tuned with LoRA+ via curriculum learning: free-form reasoning → structured template → JSON output.
- Quantization — AIMET W4A16 quantization for both the vision encoder and language decoder, exported to QNN for the Snapdragon NPU.
Datasets
GenImage (ADM, BigGAN), SID-Set, SynthScars, ImageNet, COCO train2017, ARForensics.
Files
| File | Description |
|---|---|
SSUPER-AIGID.zip |
QNN context binaries + embedding/position weights, tokenizer, and sample inputs for on-device inference. |
After extraction the package contains the serialized model binaries
(weight_sharing_model_*.serialized.bin, veg.serialized.bin), embedding_weights_151936x1536.raw,
tokenizer.json, inputs.json, and the mask / position_ids tensors.
Team
SSUPER_POWER — VIP Lab, Soongsil University Dayoung Kil · Doeon Kim · Junyoon Lee
License
First-party code is released under the MIT License. The model is derived from Alibaba's Qwen2-VL-2B (Apache-2.0) and built with Qualcomm AIMET/QNN tooling and LLaMA-Factory (Apache-2.0); please review and comply with each upstream component's license.
Citation
@misc{ssuper-aigid-2026,
title = {SSUPER-AIGID: On-Device AI-Generated Image Detection},
author = {Kil, Dayoung and Kim, Doeon and Lee, Junyoon},
year = {2026},
note = {2nd place, LPCVC 2026 Track 3 (ECV Workshop @ CVPR 2026)},
url = {https://github.com/LPCV-SSUPER-POWER/Track3-AI-Generated-Images-Detection}
}