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# inference.py
import numpy as np
from PIL import Image
import os
import sys
import json
from typing import Dict, Any
# Optional ONNX imports
try:
import onnx
import onnxruntime as ort
ONNX_AVAILABLE = True
except ImportError:
ONNX_AVAILABLE = False
print("Warning: ONNX not available. Neural network features disabled.")
# Optional Hugging Face imports
try:
from transformers import Pipeline
HF_AVAILABLE = True
except ImportError:
HF_AVAILABLE = False
print("Warning: Hugging Face transformers not available. Pipeline features disabled.")
# Add scripts directory to import utilities
scripts_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "scripts")
if scripts_dir not in sys.path:
sys.path.append(scripts_dir)
# Import unified input loader
try:
from starlight_utils import load_unified_input
except ImportError as e:
print(f"Warning: Could not import starlight_utils: {e}")
load_unified_input = None
class StarlightModel:
def __init__(
self,
detector_path: str = "model/detector.onnx",
task: str = "detect"
):
self.detector_path = detector_path
self.task = task
# Load ONNX model
if ONNX_AVAILABLE:
providers = []
available_providers = ort.get_available_providers()
if 'CUDAExecutionProvider' in available_providers:
providers.append('CUDAExecutionProvider')
if 'CoreMLExecutionProvider' in available_providers:
providers.append('CoreMLExecutionProvider')
providers.append('CPUExecutionProvider')
session_options = ort.SessionOptions()
if 'CUDAExecutionProvider' in providers:
session_options.enable_mem_pattern = False
elif 'CoreMLExecutionProvider' in providers:
session_options.enable_mem_pattern = False
if os.path.exists(detector_path):
try:
self.detector = ort.InferenceSession(detector_path, sess_options=session_options, providers=providers)
except Exception as e:
print(f"Warning: Could not load detector: {e}")
self.detector = None
else:
print(f"Warning: Detector model not found at {detector_path}")
self.detector = None
else:
self.detector = None
def _detect_method_from_filename(self, img_path: str) -> str:
basename = os.path.basename(img_path)
parts = basename.split("_")
if len(parts) >= 3:
method = parts[-2] # e.g., alpha, eoi, dct
return method
return "lsb" # Default fallback
def predict(self, img_path: str, method: str = None) -> Dict[str, Any]:
if not load_unified_input:
return {"error": "starlight_utils not available"}
# Use unified input loader (aligned with scanner.py design)
pixel_tensor, meta, alpha, lsb, palette, format_features, content_features = load_unified_input(img_path, fast_mode=True)
# Convert to numpy for ONNX and add batch dimension
# Note: lsb and alpha need to be in CHW format for ONNX
lsb_chw = lsb.permute(2, 0, 1) if lsb.dim() == 3 else lsb # (3, 256, 256)
alpha_chw = alpha.unsqueeze(0) if alpha.dim() == 2 else alpha # (1, 256, 256)
inputs = {
'meta': np.expand_dims(meta.numpy(), 0),
'alpha': np.expand_dims(alpha_chw.numpy(), 0),
'lsb': np.expand_dims(lsb_chw.numpy(), 0),
'palette': np.expand_dims(palette.numpy(), 0),
'format_features': np.expand_dims(format_features.numpy(), 0),
'content_features': np.expand_dims(content_features.numpy(), 0),
'bit_order': np.array([[0.0, 1.0, 0.0]], dtype=np.float32) # Default msb-first
}
method = method or self._detect_method_from_filename(img_path)
if self.task == "detect":
if self.detector:
try:
outputs = self.detector.run(None, inputs)
stego_logits = outputs[0]
method_logits = outputs[1]
method_id = outputs[2]
method_probs = outputs[3]
prob = float(1 / (1 + np.exp(-stego_logits[0][0]))) # Sigmoid
predicted_method = int(np.argmax(method_logits[0]))
return {
"image_path": img_path,
"stego_probability": prob,
"task": self.task,
"method": method,
"predicted_method_id": predicted_method,
"predicted": prob > 0.5
}
except Exception as e:
return {"error": f"ONNX inference failed: {e}"}
else:
return {"error": "Detector model not loaded"}
else:
return {"error": f"Task '{self.task}' not supported in unified design"}
if ONNX_AVAILABLE and load_unified_input:
class StarlightSteganographyDetectionPipeline:
def __init__(self, model_path=None, config_path="config.json", **kwargs):
# Load config
if not os.path.exists(config_path):
raise FileNotFoundError(f"Config file not found at {config_path}")
with open(config_path, 'r') as f:
self.config = json.load(f)
if model_path is None:
model_path = self.config.get("model_path", "models/detector_balanced.onnx")
# Load ONNX model
providers = []
available_providers = ort.get_available_providers()
if 'CUDAExecutionProvider' in available_providers:
providers.append('CUDAExecutionProvider')
if 'CoreMLExecutionProvider' in available_providers:
providers.append('CoreMLExecutionProvider')
providers.append('CPUExecutionProvider')
session_options = ort.SessionOptions()
if 'CUDAExecutionProvider' in providers:
session_options.enable_mem_pattern = False
elif 'CoreMLExecutionProvider' in providers:
session_options.enable_mem_pattern = False
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model not found at {model_path}")
self.model = ort.InferenceSession(model_path, sess_options=session_options, providers=providers)
def __call__(self, image_path, **kwargs):
sanitized_kwargs, _, _ = self._sanitize_parameters(**kwargs)
model_inputs = self.preprocess(image_path)
model_outputs = self._forward(model_inputs)
return self.postprocess(model_outputs)
def _sanitize_parameters(self, **kwargs):
# No specific parameters to sanitize for now
return {}, {}, {}
def preprocess(self, image_path):
if not isinstance(image_path, str) or not os.path.exists(image_path):
raise ValueError(f"Invalid image_path: {image_path}")
# Use unified input loader
try:
pixel_tensor, meta, alpha, lsb, palette, format_features, content_features = load_unified_input(image_path, fast_mode=True)
except Exception as e:
raise ValueError(f"Failed to preprocess image {image_path}: {e}")
# Convert to numpy for ONNX and add batch dimension
# Note: lsb and alpha need to be in CHW format for ONNX
lsb_chw = lsb.permute(2, 0, 1) if lsb.dim() == 3 else lsb # (3, 256, 256)
alpha_chw = alpha.unsqueeze(0) if alpha.dim() == 2 else alpha # (1, 256, 256)
model_inputs = {
'meta': np.expand_dims(meta.numpy(), 0),
'alpha': np.expand_dims(alpha_chw.numpy(), 0),
'lsb': np.expand_dims(lsb_chw.numpy(), 0),
'palette': np.expand_dims(palette.numpy(), 0),
'format_features': np.expand_dims(format_features.numpy(), 0),
'content_features': np.expand_dims(content_features.numpy(), 0),
'bit_order': np.array([[0.0, 1.0, 0.0]], dtype=np.float32) # Default msb-first
}
return model_inputs
def _forward(self, model_inputs):
try:
outputs = self.model.run(None, model_inputs)
return {
'stego_logits': outputs[0],
'method_logits': outputs[1],
}
except Exception as e:
raise RuntimeError(f"ONNX inference failed: {e}")
def postprocess(self, model_outputs):
stego_logits = model_outputs['stego_logits']
method_logits = model_outputs['method_logits']
prob = float(1 / (1 + np.exp(-stego_logits[0][0]))) # Sigmoid
method_probs = np.exp(method_logits[0]) / np.sum(np.exp(method_logits[0]))
predicted_method_id = int(np.argmax(method_logits[0]))
predicted_method_name = self.config["id2label"].get(str(predicted_method_id), "unknown")
return {
"stego_probability": prob,
"predicted_method": predicted_method_name,
"predicted_method_id": predicted_method_id,
"predicted_method_prob": float(method_probs[predicted_method_id]),
"is_steganography": prob > 0.5
}
def _sanitize_parameters(self, **kwargs):
# No specific parameters to sanitize for now
return {}, {}, {}
def preprocess(self, image_path):
if not isinstance(image_path, str) or not os.path.exists(image_path):
raise ValueError(f"Invalid image_path: {image_path}")
# Use unified input loader
try:
pixel_tensor, meta, alpha, lsb, palette, format_features, content_features = load_unified_input(image_path, fast_mode=True)
except Exception as e:
raise ValueError(f"Failed to preprocess image {image_path}: {e}")
# Convert to numpy for ONNX and add batch dimension
# Note: lsb and alpha need to be in CHW format for ONNX
lsb_chw = lsb.permute(2, 0, 1) if lsb.dim() == 3 else lsb # (3, 256, 256)
alpha_chw = alpha.unsqueeze(0) if alpha.dim() == 2 else alpha # (1, 256, 256)
model_inputs = {
'meta': np.expand_dims(meta.numpy(), 0),
'alpha': np.expand_dims(alpha_chw.numpy(), 0),
'lsb': np.expand_dims(lsb_chw.numpy(), 0),
'palette': np.expand_dims(palette.numpy(), 0),
'format_features': np.expand_dims(format_features.numpy(), 0),
'content_features': np.expand_dims(content_features.numpy(), 0),
'bit_order': np.array([[0.0, 1.0, 0.0]], dtype=np.float32) # Default msb-first
}
return model_inputs
def _forward(self, model_inputs):
try:
outputs = self.model.run(None, model_inputs)
return {
'stego_logits': outputs[0],
'method_logits': outputs[1],
}
except Exception as e:
raise RuntimeError(f"ONNX inference failed: {e}")
def postprocess(self, model_outputs):
stego_logits = model_outputs['stego_logits']
method_logits = model_outputs['method_logits']
prob = float(1 / (1 + np.exp(-stego_logits[0][0]))) # Sigmoid
method_probs = np.exp(method_logits[0]) / np.sum(np.exp(method_logits[0]))
predicted_method_id = int(np.argmax(method_logits[0]))
predicted_method_name = self.config["id2label"].get(str(predicted_method_id), "unknown")
return {
"stego_probability": prob,
"predicted_method": predicted_method_name,
"predicted_method_id": predicted_method_id,
"predicted_method_prob": float(method_probs[predicted_method_id]),
"is_steganography": prob > 0.5
}
# Convenience functions for specific tasks
def detect_steganography(img_path):
"""Detect steganography using the unified model."""
model = StarlightModel(task="detect")
return model.predict(img_path)
def get_starlight_pipeline():
"""
Initializes and returns the StarlightSteganographyDetectionPipeline.
Raises ImportError if dependencies are not met.
"""
if not ONNX_AVAILABLE:
raise ImportError("ONNX runtime library not found. Please install it with 'pip install onnxruntime'.")
if not load_unified_input:
raise ImportError("starlight_utils could not be imported. Please ensure the 'scripts' directory is in your Python path.")
return StarlightSteganographyDetectionPipeline()
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