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README.md

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+---
+language: en
+datasets:
+- ShreyashDhoot/reward_model
+- BaiqiL/NaturalBench_Images
+- x1101/nsfw-full
+- Subh775/WeaponDetection
+---
+
+# Adversarial Image Auditor
+
+This model serves as a deep learning-based image auditor for AI safety, capable of evaluating images and interpreting aligned text prompts across multiple distinct axes:
+1. **Adversarial Safety (Binary):** Predicting whether an image is Safe or Unsafe.
+2. **Category Classification:** Placing unsafe images directly into `Safe`, `NSFW`, `Gore`, or `Weapons` categories.
+3. **Artifact / Seam Quality:** Assessing the quality of image manipulation to detect adversarial seams or diffusion artifacts.
+4. **Relative Adversarial Score:** Predicting a continuous metric of adversarial strength in an image.
+5. **Prompt Faithfulness (Contrastive InfoNCE):** Calculating a temperature-scaled contrastive probability of image–text faithfulness.
+
+## Architecture
+
+This neural auditor introduces robust contrastive alignments for multimodal safety.
+- **Vision Backbone:** Pretrained DenseNet121, modified to extract feature grids to construct dense 2x2 local spatial maps.
+- **Text Conditioning:** Simple text tokenizer with correct Cross-Attention (`key_padding_mask` integrated, Pre-LayerNorm).
+- **FiLM Modulation:** Conditions adversarial layers using timestep diffusion tokens and text feature projections directly.
+- **Output:** Decoupled safety axes generating bounding-box GradCAM predictions, Continuous InfoNCE faithfulness, and safety classifications.
+
+## Usage
+
+You can load this model along with its inference script `auditor_inference.py`:
+
+```python
+from auditor_inference import audit_image
+
+results = audit_image(
+    model_path="auditor_new_best.pth",
+    image_path="example.jpg",
+    prompt="A cute cat"
+)
+
+print(results)
+```