Spaces:
Sleeping
Sleeping
| title: FakeOut | |
| emoji: 🐨 | |
| colorFrom: gray | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.18.0 | |
| python_version: '3.13' | |
| app_file: app.py | |
| pinned: false | |
| short_description: A patch-based DL model detecting microscopic AI artifacts. | |
| # FAKEOUT: A Patch-Based Deep Learning Model for AI Image Detection | |
| FAKEOUT is a forensic computer vision application built to expose synthetic modifications and AI-generated image patterns. Unlike traditional classifiers that inspect overall image composition, FAKEOUT operates as a patch-based detector. It isolates microscopic frequency anomalies and localized pixel artifacts left behind by generative architectures. | |
| ## How It Works | |
| Traditional AI detection models often become confused by an image's overall subject matter or composition. FAKEOUT bypasses this limitation by looking at the world through a fixed window: | |
| 1. **Patch Extraction:** The engine extracts a strict, deterministic 224x224 pixel crop from the exact center of the uploaded image. | |
| 2. **Artifact Scan:** A specialized ResNet-50 architecture evaluates this dense sub-grid to scan for subtle, microscopic pixel noise, texture repetitions, and architectural anomalies typical of diffusion models and GANs. | |
| 3. **Classification:** The model calculates class probabilities to determine whether the high-frequency fingerprint matches a true photograph (REAL) or an AI generation (FAKE). | |
| --- | |
| ## Dataset & Architecture | |
| * **Core Approach:** Original work featuring a strict patch-based training and inference pipeline. | |
| * **Sourced Data:** Built by merging and curating two prominent image distribution libraries: | |
| * **Flickr30k Dataset** (8.86 GB) for diverse, high-resolution authentic photography. | |
| * **Defactify Image Dataset** (7.51 GB) for diverse synthetic and manipulated generations. | |
| * **Volume:** Combined infrastructure representing 10,000 source images expanded seamlessly into 100,000 unique validation patches during pipeline engineering. | |
| --- | |
| ## Performance & Key Metrics | |
| Evaluated against a strictly quarantined, un-leaked holdout test set containing complex wild generations: | |
| * **Overall Classification Accuracy:** 76.92% | |
| * **ROC-AUC Score:** 0.7143 | |
| * **AI-Detection Recall:** 83.33% (The model successfully catches and flags over 83% of actual AI-generated fakes). | |
| ### Model Evaluation Visualized | |
|  | |
|  | |
| ### Detailed Classification Report | |
|  | |
| --- | |
| ## Limitations & Best Results | |
| To get the most accurate results out of FAKEOUT, keep the following mechanical constraints in mind: | |
| * **Resolution Sweet Spot (512px - 1500px):** The model performs best on mid-sized dimensions (e.g., 640x832 or 880x1320). | |
| * **The 4K Danger Zone:** Ultra-high-resolution images (4K / 3840x2160) will degrade performance. Because the model's 'magnifying glass' is locked to a 224x224 footprint, it ends up scanning less than 0.6% of a 4K frame, completely losing contextual positioning. | |
| * **Centered Subjects:** The patch extraction targets the dead-center. Structural AI defects occurring exclusively on frame borders or background edges will not be parsed. | |
| * **Avoid Screenshots:** Compression passes from screenshot utilities or social media pipelines strip away the micro-level frequency data the model relies on. Always upload raw, uncompressed source files. | |
| ## Technology Stack | |
| * **Core Engine:** PyTorch, Torchvision | |
| * **Weights Format:** Safetensors (Optimized for lazy CPU execution) | |
| * **Frontend:** Gradio Web Interface | |
| * **Hosting Container:** Hugging Face Spaces | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |