exif / src /metadata_parser.py

init

1516b25 7 months ago

17.3 kB

from typing import Any, Dict, List, Optional
from src.helpers.gps_helper import GPSHelper
from src.helpers.constants import (
    ATTRIBUTE_WEIGHTS, IS_AI_THRESHOLD, AI_GENERATION_KEYWORDS, AI_EDIT_KEYWORDS, TOOL_EDIT_KEYWORDS
)
from datetime import datetime
import re
import math
from src.models import (
    ImageMetadataResult, AnalysisResult, FileInfo, Attachments, ProcessMetadata, Attribute,
    CaptureParameters, GPSParameters, CameraParameters, DeviceParameters,
    AiPromptParameters, AiGenerationParameters, EditParameters, DepthMapParameters, DownloadParameters,
    MakerNoteParameters
)

class MetadataParser:
    """
    Parses detector results into a list of attributes with weights and confidence scores.
    """

    @classmethod
    def parse(cls, detector_result: Dict[str, Any], include_raw: bool = False) -> ImageMetadataResult:
        start_time = datetime.now()
        
        attributes = []
        
        # 1. Parse Attributes
        cls._parse_capture(detector_result, attributes)
        cls._parse_gps(detector_result, attributes)
        cls._parse_camera(detector_result, attributes)
        cls._parse_ai_prompt(detector_result, attributes)
        cls._parse_ai_generation(detector_result, attributes)
        cls._parse_editing(detector_result, attributes)
        cls._parse_depth_map(detector_result, attributes)
        cls._parse_makernote(detector_result, attributes)
        
        # 2. Analyze
        analysis = cls._analyze(attributes)
        
        # 3. File Info
        file_info = cls._parse_file(detector_result)
        
        # 4. Attachments
        attachments = cls._parse_attachments(detector_result)
        
        duration = (datetime.now() - start_time).total_seconds()
        
        return ImageMetadataResult(
            analysis=analysis,
            file=file_info,
            attachments=attachments,
            attributes=attributes,
            metadata=ProcessMetadata(
                started_at=start_time,
                completed_at=datetime.now(),
                duration=duration
            ),
            raw=detector_result.get("metadata") if include_raw else None
        )

    # -------------------------------------------------------------------------
    # Attribute Parsers
    # -------------------------------------------------------------------------

    @staticmethod
    def _parse_capture(result: Dict[str, Any], attributes: List[Attribute]):
        exif = result.get("metadata", {}).get("exif", {})
        dt = None
        for key in ("DateTimeOriginal", "DateTime", "CreateDate"):
            val = exif.get(key)
            if val:
                try:
                    clean_val = str(val).split('.')[0]
                    dt = datetime.strptime(clean_val, "%Y:%m:%d %H:%M:%S")
                    break
                except:
                    pass
        
        if dt:
            attributes.append(Attribute(
                type="capture",
                weight=ATTRIBUTE_WEIGHTS.get("capture", -0.3),
                is_ai=False,
                ai_confidence=0.1, # Low confidence it's AI
                parameters=CaptureParameters(created_at=dt)
            ))

    @staticmethod
    def _safe_float(v):
        try:
            f = float(v)
            if math.isnan(f) or math.isinf(f):
                return None
            return f
        except: return None

    @staticmethod
    def _parse_gps(result: Dict[str, Any], attributes: List[Attribute]):
        exif = result.get("metadata", {}).get("exif", {})
        gps = exif.get("GPSInfo")
        if gps:
            lat = gps.get("GPSLatitude")
            lon = gps.get("GPSLongitude")
            alt = gps.get("GPSAltitude")
            lat_ref = gps.get("GPSLatitudeRef", "N")
            lon_ref = gps.get("GPSLongitudeRef", "E")

            lat_val = GPSHelper.to_decimal(lat, lat_ref)
            lon_val = GPSHelper.to_decimal(lon, lon_ref)
            alt_val = GPSHelper.parse_altitude(alt)
            dir_val = GPSHelper.parse_rational(gps.get("GPSImgDirection"))
            speed_val = GPSHelper.parse_rational(gps.get("GPSSpeed"))
            speed_ref = gps.get("GPSSpeedRef")

            attributes.append(Attribute(
                type="gps",
                weight=ATTRIBUTE_WEIGHTS.get("gps", -0.3), # Presence of GPS suggests real photo
                is_ai=False,
                ai_confidence=0.1,
                parameters=GPSParameters(
                    latitude=lat_val,
                    longitude=lon_val,
                    altitude=alt_val,
                    direction=dir_val,
                    speed=speed_val,
                    speed_unit=str(speed_ref) if speed_ref else None,
                    raw=gps
                )
            ))

    @staticmethod
    def _parse_camera(result: Dict[str, Any], attributes: List[Attribute]):
        exif = result.get("metadata", {}).get("exif", {})
        make = exif.get("Make")
        model = exif.get("Model")
        
        if make or model:
            exif_ifd = exif.get("ExifIFD", {})
            
            attributes.append(Attribute(
                type="camera",
                weight=ATTRIBUTE_WEIGHTS.get("camera", -1.0), # Strong indicators of real hardware
                is_ai=False,
                ai_confidence=0.0,
                parameters=CameraParameters(
                    make=str(make) if make else None,
                    model=str(model) if model else None,
                    software=str(exif.get("Software")) if exif.get("Software") else None,
                    lens_make=str(exif_ifd.get("LensMake")) if exif_ifd.get("LensMake") else None,
                    lens_model=str(exif_ifd.get("LensModel")) if exif_ifd.get("LensModel") else None,
                    focal_length_mm=GPSHelper.parse_rational(exif_ifd.get("FocalLength")),
                    focal_length_35mm=GPSHelper.parse_rational(exif_ifd.get("FocalLengthIn35mmFilm")),
                    aperture=GPSHelper.parse_rational(exif_ifd.get("FNumber")),
                    exposure_time=GPSHelper.parse_rational(exif_ifd.get("ExposureTime")),
                    iso=int(exif_ifd.get("ISOSpeedRatings")) if exif_ifd.get("ISOSpeedRatings") else None
                )
            ))

    @staticmethod
    def _parse_ai_prompt(result: Dict[str, Any], attributes: List[Attribute]):
        metadata = result.get("metadata", {})
        png = metadata.get("png_text", {})
        exif = metadata.get("exif", {})
        exif_ifd = exif.get("ExifIFD", {})
        
        # 1. Check Stable Diffusion PNG parameters
        if "parameters" in png:
            params = png["parameters"]
            ai_params = AiPromptParameters(raw_parameters=params)
            
            # Simple parsing
            lines = params.split('\n')
            if lines:
                ai_params.prompt = lines[0]
            
            # Negative prompt
            neg_match = re.search(r"Negative prompt: (.*?)(?:\n|$)", params, re.DOTALL)
            if neg_match:
                ai_params.negative_prompt = neg_match.group(1).strip()
            
            # Model, Seed, etc.
            if "Model:" in params:
                 m = re.search(r"Model: (.*?)(?:,|$)", params)
                 if m: ai_params.model = m.group(1)
            
            attributes.append(Attribute(
                type="ai_prompt",
                weight=ATTRIBUTE_WEIGHTS.get("ai_prompt", 1.0),
                is_ai=True,
                ai_confidence=0.99,
                comments="Stable Diffusion parameters found in PNG text",
                parameters=ai_params
            ))
            return

        # 2. Check EXIF UserComment (Common in AI JPEGs)
        user_comment = exif_ifd.get("UserComment")
        if user_comment and isinstance(user_comment, str):
            # Clean null bytes and "UNICODE" prefix
            clean_comment = user_comment.replace('\x00', '').replace('UNICODE', '').strip()
            
            # Often contains JSON or "Steps: 20, Sampler: ..."
            is_ai_indicator = any(k.lower() in clean_comment.lower() for k in ["steps:", "sampler:", "cfg scale:", "seed:", "model:", "negative prompt:"])
            if is_ai_indicator:
                attributes.append(Attribute(
                    type="ai_prompt",
                    weight=ATTRIBUTE_WEIGHTS.get("ai_prompt", 1.0),
                    is_ai=True,
                    ai_confidence=0.98,
                    comments="AI generation parameters found in UserComment",
                    parameters=AiPromptParameters(raw_parameters=clean_comment)
                ))

    @classmethod
    def _parse_ai_generation(cls, result: Dict[str, Any], attributes: List[Attribute]):
        metadata = result.get("metadata", {})
        xmp = metadata.get("xmp", {})
        png_parsed = metadata.get("png_text_parsed", {})
        
        # Gather all XMP sources
        sources = [xmp]
        if "xmp" in png_parsed:
             if isinstance(png_parsed["xmp"], dict):
                 sources.append(png_parsed["xmp"])
                 for v in png_parsed["xmp"].values():
                     if isinstance(v, dict): sources.append(v)
                     
        for source in sources:
            ds = source.get("DigitalSourceType") or source.get("DigitalSourceFileType")
            if ds:
                ds_str = str(ds)
                # Check against extended AI generation keywords using regex
                is_ai_gen = any(re.search(rf"\b{re.escape(k)}\b", ds_str, re.I) for k in AI_GENERATION_KEYWORDS)
                
                # Fallback for phrases
                if not is_ai_gen:
                    is_ai_gen = any(k.lower() in ds_str.lower() for k in ["TrainedAlgorithmicMedia", "Generative AI"])
                
                if is_ai_gen:
                     attributes.append(Attribute(
                         type="ai_generation",
                         weight=ATTRIBUTE_WEIGHTS.get("ai_generation", 1.0),
                         is_ai=True,
                         ai_confidence=0.95,
                         comments=f"AI indicator found in DigitalSourceType: {ds_str}",
                         parameters=AiGenerationParameters(
                             digital_source_type=ds_str
                         )
                     ))
                     return # Found detection

    @staticmethod
    def _parse_editing(result: Dict[str, Any], attributes: List[Attribute]):
        metadata = result.get("metadata", {})
        exif = metadata.get("exif", {})
        xmp = metadata.get("xmp", {})
        iptc = metadata.get("iptc", {})
        
        software = exif.get("Software") or xmp.get("CreatorTool") or iptc.get("IPTC:Software")
        credit = iptc.get("IPTC:Credit") or xmp.get("Credit") or exif.get("Artist")
        exif_ifd = exif.get("ExifIFD", {})
        user_comment = str(exif_ifd.get("UserComment", ""))
        
        # Combine all string-based indicators for keyword searching
        combined_text = f"{software} {credit} {user_comment} {exif.get('Make', '')} {exif.get('Model', '')}"
        
        # Case-insensitive keyword search
        is_ai_edit = any(re.search(rf"\b{re.escape(k)}\b", combined_text, re.I) for k in AI_EDIT_KEYWORDS)
        is_tool_edit = any(re.search(rf"\b{re.escape(k)}\b", combined_text, re.I) for k in TOOL_EDIT_KEYWORDS)
        
        # Special check for specific phrases that might not be full words
        if not is_ai_edit:
            is_ai_edit = any(k.lower() in combined_text.lower() for k in ["Edited with Google AI", "Generative"])
        
        history = []
        if "History" in xmp:
             hist_raw = xmp["History"]
             if isinstance(hist_raw, list): history = [str(x) for x in hist_raw]
        
        if is_ai_edit:
            attributes.append(Attribute(
                type="ai_edit",
                weight=ATTRIBUTE_WEIGHTS.get("ai_edit", 1.0),
                is_ai=True,
                ai_confidence=0.9,
                comments="AI keywords found in software/credit",
                parameters=EditParameters(
                    software=str(software) if software else None,
                    label="AI Edited",
                    history=history
                )
            ))
        elif is_tool_edit or software:
             if any(a.type == "camera" for a in attributes):
                weight = ATTRIBUTE_WEIGHTS.get("tool_edit_with_camera", -0.5) # Normal editing of real photo
             else:
                weight = ATTRIBUTE_WEIGHTS.get("tool_edit_standalone", -0.3) # Unknown source
                
             attributes.append(Attribute(
                type="tool_edit",
                weight=weight,
                is_ai=False,
                ai_confidence=0.2,
                comments="Editing software detected",
                parameters=EditParameters(
                    software=str(software) if software else None,
                    label="Tool Edited",
                    history=history
                )
            ))

    @staticmethod
    def _parse_depth_map(result: Dict[str, Any], attributes: List[Attribute]):
        depth = result.get("metadata", {}).get("exif", {}).get("additional", {}).get("depth_map")
        if depth:
             # It's an image object or path usually, from main.py it was an Image object
             # Here we just flag it
             attributes.append(Attribute(
                 type="depth_map",
                 weight=ATTRIBUTE_WEIGHTS.get("depth_map", -1.0),
                 is_ai=False,
                 ai_confidence=0.0,
                 comments="Depth map embedded (Portrait Mode)",
                 parameters=DepthMapParameters(depth_present=True)
             ))

    @staticmethod
    def _parse_makernote(result: Dict[str, Any], attributes: List[Attribute]):
        makernote = result.get("metadata", {}).get("makernote")
        if makernote:
            make = makernote.get("make", "Unknown")
            raw_data = makernote.get("raw", {})
            
            # MakerNotes are strong indicators of real hardware capture
            attributes.append(Attribute(
                type="makernote",
                weight=ATTRIBUTE_WEIGHTS.get("makernote", -1.0),
                is_ai=False,
                ai_confidence=0.0,
                comments=f"{make} MakerNotes detected",
                parameters=MakerNoteParameters(
                    make=make,
                    raw=raw_data
                )
            ))

    # -------------------------------------------------------------------------
    # Analysis & Helpers
    # -------------------------------------------------------------------------

    @staticmethod
    def _analyze(attributes: List[Attribute]) -> AnalysisResult:
        """
        Calculate final analysis based on the sum of all attribute AI confidences and weights.
        """
        ai_weighted_score = 0.0
        real_weighted_score = 0.0
        edit_score = 0.0
        
        for attr in attributes:
            if attr.weight > 0:
                # Indicators of AI
                ai_weighted_score += attr.ai_confidence * attr.weight
            else:
                # Indicators of Real (negative weight)
                weight_abs = abs(attr.weight)
                # If ai_confidence is low, it contributes highly to real_score
                real_weighted_score += (1.0 - attr.ai_confidence) * weight_abs
            
            # Additional check for editing
            if attr.type in ("ai_edit", "tool_edit"):
                edit_score = max(edit_score, 0.5 if attr.type == "tool_edit" else 0.9)

        total_score = ai_weighted_score + real_weighted_score
        
        if total_score == 0:
            return AnalysisResult(
                is_ai=False,
                ai_confidence=0.0,
                real_confidence=0.5,
                edited_confidence=edit_score
            )
            
        ai_conf = ai_weighted_score / total_score
        real_conf = real_weighted_score / total_score
        
        # Determine is_ai based on threshold
        is_ai = ai_conf > IS_AI_THRESHOLD
        
        return AnalysisResult(
            is_ai=is_ai,
            ai_confidence=round(ai_conf, 2),
            real_confidence=round(real_conf, 2),
            edited_confidence=edit_score
        )

    @staticmethod
    def _parse_file(result: Dict[str, Any]) -> FileInfo:
        size = result.get("size") or (None, None)
        return FileInfo(
            path=result.get("file_path"),
            mime_type=result.get("mime_type"),
            image_format=result.get("image_format"),
            width=size[0],
            height=size[1],
            size_bytes=result.get("size_bytes"),
            color_space=result.get("color_space")
        )

    @staticmethod
    def _parse_attachments(result: Dict[str, Any]) -> Attachments:
        att = Attachments()
        # If we saved depth map to disk, we would link it here.
        # For now, just checking if it exists
        if result.get("metadata", {}).get("exif", {}).get("additional", {}).get("depth_map"):
            att.depth_map = "embedded"
        return att

	from typing import Any, Dict, List, Optional
	from src.helpers.gps_helper import GPSHelper
	from src.helpers.constants import (
	ATTRIBUTE_WEIGHTS, IS_AI_THRESHOLD, AI_GENERATION_KEYWORDS, AI_EDIT_KEYWORDS, TOOL_EDIT_KEYWORDS
	)
	from datetime import datetime
	import re
	import math
	from src.models import (
	ImageMetadataResult, AnalysisResult, FileInfo, Attachments, ProcessMetadata, Attribute,
	CaptureParameters, GPSParameters, CameraParameters, DeviceParameters,
	AiPromptParameters, AiGenerationParameters, EditParameters, DepthMapParameters, DownloadParameters,
	MakerNoteParameters
	)

	class MetadataParser:
	"""
	Parses detector results into a list of attributes with weights and confidence scores.
	"""

	@classmethod
	def parse(cls, detector_result: Dict[str, Any], include_raw: bool = False) -> ImageMetadataResult:
	start_time = datetime.now()

	attributes = []

	# 1. Parse Attributes
	cls._parse_capture(detector_result, attributes)
	cls._parse_gps(detector_result, attributes)
	cls._parse_camera(detector_result, attributes)
	cls._parse_ai_prompt(detector_result, attributes)
	cls._parse_ai_generation(detector_result, attributes)
	cls._parse_editing(detector_result, attributes)
	cls._parse_depth_map(detector_result, attributes)
	cls._parse_makernote(detector_result, attributes)

	# 2. Analyze
	analysis = cls._analyze(attributes)

	# 3. File Info
	file_info = cls._parse_file(detector_result)

	# 4. Attachments
	attachments = cls._parse_attachments(detector_result)

	duration = (datetime.now() - start_time).total_seconds()

	return ImageMetadataResult(
	analysis=analysis,
	file=file_info,
	attachments=attachments,
	attributes=attributes,
	metadata=ProcessMetadata(
	started_at=start_time,
	completed_at=datetime.now(),
	duration=duration
	),
	raw=detector_result.get("metadata") if include_raw else None
	)

	# -------------------------------------------------------------------------
	# Attribute Parsers
	# -------------------------------------------------------------------------

	@staticmethod
	def _parse_capture(result: Dict[str, Any], attributes: List[Attribute]):
	exif = result.get("metadata", {}).get("exif", {})
	dt = None
	for key in ("DateTimeOriginal", "DateTime", "CreateDate"):
	val = exif.get(key)
	if val:
	try:
	clean_val = str(val).split('.')[0]
	dt = datetime.strptime(clean_val, "%Y:%m:%d %H:%M:%S")
	break
	except:
	pass

	if dt:
	attributes.append(Attribute(
	type="capture",
	weight=ATTRIBUTE_WEIGHTS.get("capture", -0.3),
	is_ai=False,
	ai_confidence=0.1, # Low confidence it's AI
	parameters=CaptureParameters(created_at=dt)
	))

	@staticmethod
	def _safe_float(v):
	try:
	f = float(v)
	if math.isnan(f) or math.isinf(f):
	return None
	return f
	except: return None

	@staticmethod
	def _parse_gps(result: Dict[str, Any], attributes: List[Attribute]):
	exif = result.get("metadata", {}).get("exif", {})
	gps = exif.get("GPSInfo")
	if gps:
	lat = gps.get("GPSLatitude")
	lon = gps.get("GPSLongitude")
	alt = gps.get("GPSAltitude")
	lat_ref = gps.get("GPSLatitudeRef", "N")
	lon_ref = gps.get("GPSLongitudeRef", "E")

	lat_val = GPSHelper.to_decimal(lat, lat_ref)
	lon_val = GPSHelper.to_decimal(lon, lon_ref)
	alt_val = GPSHelper.parse_altitude(alt)
	dir_val = GPSHelper.parse_rational(gps.get("GPSImgDirection"))
	speed_val = GPSHelper.parse_rational(gps.get("GPSSpeed"))
	speed_ref = gps.get("GPSSpeedRef")

	attributes.append(Attribute(
	type="gps",
	weight=ATTRIBUTE_WEIGHTS.get("gps", -0.3), # Presence of GPS suggests real photo
	is_ai=False,
	ai_confidence=0.1,
	parameters=GPSParameters(
	latitude=lat_val,
	longitude=lon_val,
	altitude=alt_val,
	direction=dir_val,
	speed=speed_val,
	speed_unit=str(speed_ref) if speed_ref else None,
	raw=gps
	)
	))

	@staticmethod
	def _parse_camera(result: Dict[str, Any], attributes: List[Attribute]):
	exif = result.get("metadata", {}).get("exif", {})
	make = exif.get("Make")
	model = exif.get("Model")

	if make or model:
	exif_ifd = exif.get("ExifIFD", {})

	attributes.append(Attribute(
	type="camera",
	weight=ATTRIBUTE_WEIGHTS.get("camera", -1.0), # Strong indicators of real hardware
	is_ai=False,
	ai_confidence=0.0,
	parameters=CameraParameters(
	make=str(make) if make else None,
	model=str(model) if model else None,
	software=str(exif.get("Software")) if exif.get("Software") else None,
	lens_make=str(exif_ifd.get("LensMake")) if exif_ifd.get("LensMake") else None,
	lens_model=str(exif_ifd.get("LensModel")) if exif_ifd.get("LensModel") else None,
	focal_length_mm=GPSHelper.parse_rational(exif_ifd.get("FocalLength")),
	focal_length_35mm=GPSHelper.parse_rational(exif_ifd.get("FocalLengthIn35mmFilm")),
	aperture=GPSHelper.parse_rational(exif_ifd.get("FNumber")),
	exposure_time=GPSHelper.parse_rational(exif_ifd.get("ExposureTime")),
	iso=int(exif_ifd.get("ISOSpeedRatings")) if exif_ifd.get("ISOSpeedRatings") else None
	)
	))

	@staticmethod
	def _parse_ai_prompt(result: Dict[str, Any], attributes: List[Attribute]):
	metadata = result.get("metadata", {})
	png = metadata.get("png_text", {})
	exif = metadata.get("exif", {})
	exif_ifd = exif.get("ExifIFD", {})

	# 1. Check Stable Diffusion PNG parameters
	if "parameters" in png:
	params = png["parameters"]
	ai_params = AiPromptParameters(raw_parameters=params)

	# Simple parsing
	lines = params.split('\n')
	if lines:
	ai_params.prompt = lines[0]

	# Negative prompt
	neg_match = re.search(r"Negative prompt: (.*?)(?:\n\|$)", params, re.DOTALL)
	if neg_match:
	ai_params.negative_prompt = neg_match.group(1).strip()

	# Model, Seed, etc.
	if "Model:" in params:
	m = re.search(r"Model: (.*?)(?:,\|$)", params)
	if m: ai_params.model = m.group(1)

	attributes.append(Attribute(
	type="ai_prompt",
	weight=ATTRIBUTE_WEIGHTS.get("ai_prompt", 1.0),
	is_ai=True,
	ai_confidence=0.99,
	comments="Stable Diffusion parameters found in PNG text",
	parameters=ai_params
	))
	return

	# 2. Check EXIF UserComment (Common in AI JPEGs)
	user_comment = exif_ifd.get("UserComment")
	if user_comment and isinstance(user_comment, str):
	# Clean null bytes and "UNICODE" prefix
	clean_comment = user_comment.replace('\x00', '').replace('UNICODE', '').strip()

	# Often contains JSON or "Steps: 20, Sampler: ..."
	is_ai_indicator = any(k.lower() in clean_comment.lower() for k in ["steps:", "sampler:", "cfg scale:", "seed:", "model:", "negative prompt:"])
	if is_ai_indicator:
	attributes.append(Attribute(
	type="ai_prompt",
	weight=ATTRIBUTE_WEIGHTS.get("ai_prompt", 1.0),
	is_ai=True,
	ai_confidence=0.98,
	comments="AI generation parameters found in UserComment",
	parameters=AiPromptParameters(raw_parameters=clean_comment)
	))

	@classmethod
	def _parse_ai_generation(cls, result: Dict[str, Any], attributes: List[Attribute]):
	metadata = result.get("metadata", {})
	xmp = metadata.get("xmp", {})
	png_parsed = metadata.get("png_text_parsed", {})

	# Gather all XMP sources
	sources = [xmp]
	if "xmp" in png_parsed:
	if isinstance(png_parsed["xmp"], dict):
	sources.append(png_parsed["xmp"])
	for v in png_parsed["xmp"].values():
	if isinstance(v, dict): sources.append(v)

	for source in sources:
	ds = source.get("DigitalSourceType") or source.get("DigitalSourceFileType")
	if ds:
	ds_str = str(ds)
	# Check against extended AI generation keywords using regex
	is_ai_gen = any(re.search(rf"\b{re.escape(k)}\b", ds_str, re.I) for k in AI_GENERATION_KEYWORDS)

	# Fallback for phrases
	if not is_ai_gen:
	is_ai_gen = any(k.lower() in ds_str.lower() for k in ["TrainedAlgorithmicMedia", "Generative AI"])

	if is_ai_gen:
	attributes.append(Attribute(
	type="ai_generation",
	weight=ATTRIBUTE_WEIGHTS.get("ai_generation", 1.0),
	is_ai=True,
	ai_confidence=0.95,
	comments=f"AI indicator found in DigitalSourceType: {ds_str}",
	parameters=AiGenerationParameters(
	digital_source_type=ds_str
	)
	))
	return # Found detection

	@staticmethod
	def _parse_editing(result: Dict[str, Any], attributes: List[Attribute]):
	metadata = result.get("metadata", {})
	exif = metadata.get("exif", {})
	xmp = metadata.get("xmp", {})
	iptc = metadata.get("iptc", {})

	software = exif.get("Software") or xmp.get("CreatorTool") or iptc.get("IPTC:Software")
	credit = iptc.get("IPTC:Credit") or xmp.get("Credit") or exif.get("Artist")
	exif_ifd = exif.get("ExifIFD", {})
	user_comment = str(exif_ifd.get("UserComment", ""))

	# Combine all string-based indicators for keyword searching
	combined_text = f"{software} {credit} {user_comment} {exif.get('Make', '')} {exif.get('Model', '')}"

	# Case-insensitive keyword search
	is_ai_edit = any(re.search(rf"\b{re.escape(k)}\b", combined_text, re.I) for k in AI_EDIT_KEYWORDS)
	is_tool_edit = any(re.search(rf"\b{re.escape(k)}\b", combined_text, re.I) for k in TOOL_EDIT_KEYWORDS)

	# Special check for specific phrases that might not be full words
	if not is_ai_edit:
	is_ai_edit = any(k.lower() in combined_text.lower() for k in ["Edited with Google AI", "Generative"])

	history = []
	if "History" in xmp:
	hist_raw = xmp["History"]
	if isinstance(hist_raw, list): history = [str(x) for x in hist_raw]

	if is_ai_edit:
	attributes.append(Attribute(
	type="ai_edit",
	weight=ATTRIBUTE_WEIGHTS.get("ai_edit", 1.0),
	is_ai=True,
	ai_confidence=0.9,
	comments="AI keywords found in software/credit",
	parameters=EditParameters(
	software=str(software) if software else None,
	label="AI Edited",
	history=history
	)
	))
	elif is_tool_edit or software:
	if any(a.type == "camera" for a in attributes):
	weight = ATTRIBUTE_WEIGHTS.get("tool_edit_with_camera", -0.5) # Normal editing of real photo
	else:
	weight = ATTRIBUTE_WEIGHTS.get("tool_edit_standalone", -0.3) # Unknown source

	attributes.append(Attribute(
	type="tool_edit",
	weight=weight,
	is_ai=False,
	ai_confidence=0.2,
	comments="Editing software detected",
	parameters=EditParameters(
	software=str(software) if software else None,
	label="Tool Edited",
	history=history
	)
	))

	@staticmethod
	def _parse_depth_map(result: Dict[str, Any], attributes: List[Attribute]):
	depth = result.get("metadata", {}).get("exif", {}).get("additional", {}).get("depth_map")
	if depth:
	# It's an image object or path usually, from main.py it was an Image object
	# Here we just flag it
	attributes.append(Attribute(
	type="depth_map",
	weight=ATTRIBUTE_WEIGHTS.get("depth_map", -1.0),
	is_ai=False,
	ai_confidence=0.0,
	comments="Depth map embedded (Portrait Mode)",
	parameters=DepthMapParameters(depth_present=True)
	))

	@staticmethod
	def _parse_makernote(result: Dict[str, Any], attributes: List[Attribute]):
	makernote = result.get("metadata", {}).get("makernote")
	if makernote:
	make = makernote.get("make", "Unknown")
	raw_data = makernote.get("raw", {})

	# MakerNotes are strong indicators of real hardware capture
	attributes.append(Attribute(
	type="makernote",
	weight=ATTRIBUTE_WEIGHTS.get("makernote", -1.0),
	is_ai=False,
	ai_confidence=0.0,
	comments=f"{make} MakerNotes detected",
	parameters=MakerNoteParameters(
	make=make,
	raw=raw_data
	)
	))

	# -------------------------------------------------------------------------
	# Analysis & Helpers
	# -------------------------------------------------------------------------

	@staticmethod
	def _analyze(attributes: List[Attribute]) -> AnalysisResult:
	"""
	Calculate final analysis based on the sum of all attribute AI confidences and weights.
	"""
	ai_weighted_score = 0.0
	real_weighted_score = 0.0
	edit_score = 0.0

	for attr in attributes:
	if attr.weight > 0:
	# Indicators of AI
	ai_weighted_score += attr.ai_confidence * attr.weight
	else:
	# Indicators of Real (negative weight)
	weight_abs = abs(attr.weight)
	# If ai_confidence is low, it contributes highly to real_score
	real_weighted_score += (1.0 - attr.ai_confidence) * weight_abs

	# Additional check for editing
	if attr.type in ("ai_edit", "tool_edit"):
	edit_score = max(edit_score, 0.5 if attr.type == "tool_edit" else 0.9)

	total_score = ai_weighted_score + real_weighted_score

	if total_score == 0:
	return AnalysisResult(
	is_ai=False,
	ai_confidence=0.0,
	real_confidence=0.5,
	edited_confidence=edit_score
	)

	ai_conf = ai_weighted_score / total_score
	real_conf = real_weighted_score / total_score

	# Determine is_ai based on threshold
	is_ai = ai_conf > IS_AI_THRESHOLD

	return AnalysisResult(
	is_ai=is_ai,
	ai_confidence=round(ai_conf, 2),
	real_confidence=round(real_conf, 2),
	edited_confidence=edit_score
	)

	@staticmethod
	def _parse_file(result: Dict[str, Any]) -> FileInfo:
	size = result.get("size") or (None, None)
	return FileInfo(
	path=result.get("file_path"),
	mime_type=result.get("mime_type"),
	image_format=result.get("image_format"),
	width=size[0],
	height=size[1],
	size_bytes=result.get("size_bytes"),
	color_space=result.get("color_space")
	)

	@staticmethod
	def _parse_attachments(result: Dict[str, Any]) -> Attachments:
	att = Attachments()
	# If we saved depth map to disk, we would link it here.
	# For now, just checking if it exists
	if result.get("metadata", {}).get("exif", {}).get("additional", {}).get("depth_map"):
	att.depth_map = "embedded"
	return att