image-antispoofing

Image Anti-Spoofing System

Detects whether an image is real or fake (AI-generated OR deepfake/manipulated).

Third project in a multi-modal anti-spoofing portfolio, alongside Voice-Anti-Spoofing and Text-Anti-Spoofing.

Architectures

Three architectures were trained and compared on the same data:

Training data

prithivMLmods/AI-vs-Deepfake-vs-Real — original 3-way labels (Artificial, Deepfake, Real) merged into a binary task: Real (0) vs Fake (1, covering both AI-generated and deepfake images), to detect AI manipulation of any kind in one model.

Results (test set accuracy)

Important caveat on the ViT's 100% score: a perfect test-set score is a signal to scrutinize, not just celebrate. It likely reflects that this dataset's fake images (from a specific, fixed set of generators) have consistent, learnable artifacts, rather than the model having solved "detect any AI-manipulated image" universally. Performance against newer/unseen generators (e.g. Midjourney v6, Flux, Stable Diffusion 3, or generators not represented in this training set) is untested and likely to be lower - this is a common "concept drift" issue in this field, since fake-image generators keep improving and older detectors don't automatically generalize to them.

Recommended checkpoint for deployment: deeper_cnn_model.pth. At 99.20% accuracy and ~26MB (vs. ViT's 343MB), it offers a strong practical tradeoff between accuracy and deployment cost (faster cold starts, less bandwidth) with a more plausible, less potentially-overfit accuracy figure than the ViT's perfect score.

Files in this repo

• baseline_cnn_model.pth
• deeper_cnn_model.pth (recommended)
• vit_model.pth
• model.py - architecture class definitions + preprocessing pipeline, required to load any of the above

Input format

• Any image PIL can open (.jpg, .png, etc.)
• Resized to 224x224
• Normalized with standard ImageNet mean/std

model.py includes a preprocess_image() function that handles all of this automatically.

Usage

from huggingface_hub import hf_hub_download
import sys

model_py_path = hf_hub_download(repo_id="LaabhGupta/image-antispoofing", filename="model.py")
weights_path = hf_hub_download(repo_id="LaabhGupta/image-antispoofing", filename="deeper_cnn_model.pth")

sys.path.insert(0, model_py_path.rsplit("/", 1)[0])
from model import load_model, predict

model = load_model("deeper", weights_path, device="cpu")
label, confidence = predict("path/to/image.jpg", model, device="cpu")
print(label, confidence)