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Image Signals API

FrequencySignal

Bases: BaseSignal

Detects AI images using frequency domain analysis (FFT).

AI-generated images often exhibit specific artifacts in the frequency domain, such as regular grid-like patterns (checkerboard artifacts) from upsampling layers or anomalous power distributions compared to natural images.

This signal computes the Fourier Transform of the image and extracts features like: - Mean frequency magnitude - High-frequency energy ratio - Variance of the Laplacian (sharpness/texture)

Attributes:

Name Type Description
name str

'frequency_artifacts'
dtype str

'image'
Source code in veridex/image/frequency.py 
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class FrequencySignal(BaseSignal):
    """
    Detects AI images using frequency domain analysis (FFT).

    AI-generated images often exhibit specific artifacts in the frequency domain,
    such as regular grid-like patterns (checkerboard artifacts) from upsampling layers
    or anomalous power distributions compared to natural images.

    This signal computes the Fourier Transform of the image and extracts features like:
    - Mean frequency magnitude
    - High-frequency energy ratio
    - Variance of the Laplacian (sharpness/texture)

    Attributes:
        name (str): 'frequency_artifacts'
        dtype (str): 'image'
    """

    @property
    def name(self) -> str:
        return "frequency_artifacts"

    @property
    def dtype(self) -> str:
        return "image"

    def check_dependencies(self) -> None:
        try:
            import cv2
            import numpy
        except ImportError as e:
            raise ImportError(
                "FrequencySignal requires 'opencv-python-headless' and 'numpy'. "
                "Install with `pip install veridex[image]`"
            ) from e

    def run(self, input_data: Any) -> DetectionResult:
        """
        Input data should be a path to an image or a numpy array/PIL Image.
        For simplicity, we assume input_data is a file path or a PIL Image.
        """
        try:
            import cv2
            from PIL import Image
        except ImportError:
            self.check_dependencies()
            # If check_dependencies passed but import fails (unlikely), re-raise
            raise

        img_array = None

        # Handle input types
        if isinstance(input_data, str):
            try:
                # Load as grayscale
                img_array = cv2.imread(input_data, cv2.IMREAD_GRAYSCALE)
                if img_array is None:
                    return DetectionResult(
                        score=0.0,
                        confidence=0.0,
                        metadata={},
                        error=f"Could not read image from path: {input_data}"
                    )
            except Exception as e:
                return DetectionResult(
                     score=0.0,
                     confidence=0.0,
                     metadata={},
                     error=f"Error reading image path: {str(e)}"
                )
        elif isinstance(input_data, Image.Image):
            # Convert PIL to grayscale numpy array
            img_array = np.array(input_data.convert("L"))
        elif isinstance(input_data, np.ndarray):
             # Assume it's an image array. If 3 channels, convert to gray.
             if len(input_data.shape) == 3:
                 img_array = cv2.cvtColor(input_data, cv2.COLOR_BGR2GRAY)
             else:
                 img_array = input_data
        else:
            return DetectionResult(
                score=0.0,
                confidence=0.0,
                metadata={},
                error="Input must be a file path, PIL Image, or numpy array."
            )

        # 1. FFT
        f = np.fft.fft2(img_array)
        fshift = np.fft.fftshift(f)
        magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1e-8)

        # 2. Calculate metrics
        # Mean frequency magnitude
        mean_magnitude = np.mean(magnitude_spectrum)

        # High frequency ratio (heuristic)
        rows, cols = img_array.shape
        crow, ccol = rows // 2, cols // 2
        # Mask low frequencies (center)
        mask_size = min(rows, cols) // 8
        fshift_high = fshift.copy()
        fshift_high[crow - mask_size:crow + mask_size, ccol - mask_size:ccol + mask_size] = 0

        high_freq_energy = np.sum(np.abs(fshift_high))
        total_energy = np.sum(np.abs(fshift))

        high_freq_ratio = high_freq_energy / (total_energy + 1e-8)

        # Variance of Laplacian (blur detection / high freq texture)
        laplacian_var = cv2.Laplacian(img_array, cv2.CV_64F).var()

        # Heuristic scoring (placeholder logic)
        # Real images often have specific 1/f decay.
        # AI images might have higher high-freq energy due to upsampling artifacts (checkerboard)
        # OR they might be overly smooth (low laplacian var).
        # This is highly model dependent.
        # For now, we return a neutral score but provide rich metadata.
        # Confidence is low (0.3) since this is raw heuristic without trained model

        return DetectionResult(
            score=0.5,
            confidence=0.3,  # Low confidence - heuristic frequency analysis
            metadata={
                "mean_magnitude": float(mean_magnitude),
                "high_freq_ratio": float(high_freq_ratio),
                "laplacian_variance": float(laplacian_var),
                "image_shape": img_array.shape
            }
        )

run(input_data)

Input data should be a path to an image or a numpy array/PIL Image. For simplicity, we assume input_data is a file path or a PIL Image.

Source code in veridex/image/frequency.py 
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def run(self, input_data: Any) -> DetectionResult:
    """
    Input data should be a path to an image or a numpy array/PIL Image.
    For simplicity, we assume input_data is a file path or a PIL Image.
    """
    try:
        import cv2
        from PIL import Image
    except ImportError:
        self.check_dependencies()
        # If check_dependencies passed but import fails (unlikely), re-raise
        raise

    img_array = None

    # Handle input types
    if isinstance(input_data, str):
        try:
            # Load as grayscale
            img_array = cv2.imread(input_data, cv2.IMREAD_GRAYSCALE)
            if img_array is None:
                return DetectionResult(
                    score=0.0,
                    confidence=0.0,
                    metadata={},
                    error=f"Could not read image from path: {input_data}"
                )
        except Exception as e:
            return DetectionResult(
                 score=0.0,
                 confidence=0.0,
                 metadata={},
                 error=f"Error reading image path: {str(e)}"
            )
    elif isinstance(input_data, Image.Image):
        # Convert PIL to grayscale numpy array
        img_array = np.array(input_data.convert("L"))
    elif isinstance(input_data, np.ndarray):
         # Assume it's an image array. If 3 channels, convert to gray.
         if len(input_data.shape) == 3:
             img_array = cv2.cvtColor(input_data, cv2.COLOR_BGR2GRAY)
         else:
             img_array = input_data
    else:
        return DetectionResult(
            score=0.0,
            confidence=0.0,
            metadata={},
            error="Input must be a file path, PIL Image, or numpy array."
        )

    # 1. FFT
    f = np.fft.fft2(img_array)
    fshift = np.fft.fftshift(f)
    magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1e-8)

    # 2. Calculate metrics
    # Mean frequency magnitude
    mean_magnitude = np.mean(magnitude_spectrum)

    # High frequency ratio (heuristic)
    rows, cols = img_array.shape
    crow, ccol = rows // 2, cols // 2
    # Mask low frequencies (center)
    mask_size = min(rows, cols) // 8
    fshift_high = fshift.copy()
    fshift_high[crow - mask_size:crow + mask_size, ccol - mask_size:ccol + mask_size] = 0

    high_freq_energy = np.sum(np.abs(fshift_high))
    total_energy = np.sum(np.abs(fshift))

    high_freq_ratio = high_freq_energy / (total_energy + 1e-8)

    # Variance of Laplacian (blur detection / high freq texture)
    laplacian_var = cv2.Laplacian(img_array, cv2.CV_64F).var()

    # Heuristic scoring (placeholder logic)
    # Real images often have specific 1/f decay.
    # AI images might have higher high-freq energy due to upsampling artifacts (checkerboard)
    # OR they might be overly smooth (low laplacian var).
    # This is highly model dependent.
    # For now, we return a neutral score but provide rich metadata.
    # Confidence is low (0.3) since this is raw heuristic without trained model

    return DetectionResult(
        score=0.5,
        confidence=0.3,  # Low confidence - heuristic frequency analysis
        metadata={
            "mean_magnitude": float(mean_magnitude),
            "high_freq_ratio": float(high_freq_ratio),
            "laplacian_variance": float(laplacian_var),
            "image_shape": img_array.shape
        }
    )

DIRESignal

Bases: BaseSignal

Detects AI images using Diffusion Reconstruction Error (DIRE).

Based on the hypothesis that diffusion models can reconstruct images they generated (or similar ones) more accurately than real natural images.

Methodology
  1. Take input image I.
  2. Add noise to obtain I_noisy (simulating diffusion forward step).
  3. Denoise I_noisy using a pre-trained diffusion model to get I_rec.
  4. Calculate Reconstruction Error = |I - I_rec|.
  5. Low Error -> Likely AI (on the model's manifold).
  6. High Error -> Likely Real (harder to reconstruct).
Note

This is a simplified approximation using Image-to-Image translation with low strength as a proxy for the full DDIM inversion process described in the original paper.

Attributes:

Name Type Description
name str

'dire_reconstruction'
dtype str

'image'
model_id str

HuggingFace Diffusion model ID.
Source code in veridex/image/dire.py 
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class DIRESignal(BaseSignal):
    """
    Detects AI images using Diffusion Reconstruction Error (DIRE).

    Based on the hypothesis that diffusion models can reconstruct images they generated
    (or similar ones) more accurately than real natural images.

    Methodology:
        1. Take input image I.
        2. Add noise to obtain I_noisy (simulating diffusion forward step).
        3. Denoise I_noisy using a pre-trained diffusion model to get I_rec.
        4. Calculate Reconstruction Error = |I - I_rec|.
        - Low Error -> Likely AI (on the model's manifold).
        - High Error -> Likely Real (harder to reconstruct).

    Note:
        This is a simplified approximation using Image-to-Image translation with low strength
        as a proxy for the full DDIM inversion process described in the original paper.

    Attributes:
        name (str): 'dire_reconstruction'
        dtype (str): 'image'
        model_id (str): HuggingFace Diffusion model ID.
    """

    def __init__(self, model_id: str = "runwayml/stable-diffusion-v1-5", device: str = "cpu"):
        """
        Initialize the DIRE signal.

        Args:
            model_id (str): The Stable Diffusion model to use for reconstruction.
            device (str): Computation device ('cpu' or 'cuda').
        """
        self.model_id = model_id
        self.device = device
        self._pipeline = None

    @property
    def name(self) -> str:
        return "dire_reconstruction"

    @property
    def dtype(self) -> str:
        return "image"

    def check_dependencies(self) -> None:
        try:
            import torch
            import diffusers
            import transformers
        except ImportError as e:
            raise ImportError(
                "DIRESignal requires 'torch', 'diffusers', and 'transformers'. "
                "Install with `pip install veridex[image]`"
            ) from e

    def _load_pipeline(self):
        if self._pipeline is not None:
            return self._pipeline

        self.check_dependencies()
        import torch
        from diffusers import StableDiffusionImg2ImgPipeline

        # Note: In a real production environment, we should handle model caching carefully.
        # This will download the model if not present.
        try:
            dtype = torch.float16 if self.device == "cuda" else torch.float32
            self._pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
                self.model_id,
                torch_dtype=dtype,
                safety_checker=None, # Disable for speed/raw reconstruction
                requires_safety_checker=False
            )
            self._pipeline.to(self.device)
            # Disable progress bar for cleaner logs
            self._pipeline.set_progress_bar_config(disable=True)
        except Exception as e:
            raise RuntimeError(f"Failed to load diffusion model: {e}")

        return self._pipeline

    def run(self, input_data: Any) -> DetectionResult:
        try:
            from PIL import Image
            import numpy as np
            import torch
        except ImportError:
            self.check_dependencies()
            raise

        # 1. Prepare Input
        image = None
        if isinstance(input_data, str):
            try:
                image = Image.open(input_data).convert("RGB")
            except Exception as e:
                return DetectionResult(
                    score=0.0, confidence=0.0, metadata={},
                    error=f"Could not open image: {e}"
                )
        elif isinstance(input_data, Image.Image):
            image = input_data.convert("RGB")
        elif isinstance(input_data, np.ndarray):
             image = Image.fromarray(input_data).convert("RGB")
        else:
             return DetectionResult(
                score=0.0, confidence=0.0, metadata={},
                error="Input must be file path, PIL Image, or numpy array."
            )

        # Resize for SD (usually 512x512)
        original_size = image.size
        image_resized = image.resize((512, 512), Image.BICUBIC)

        # 2. Run Reconstruction
        try:
            pipe = self._load_pipeline()

            # DIRE steps:
            # In paper: Inversion (x -> z) then Reconstruction (z -> x_hat).
            # Simplified approximation for this library:
            # Use SDEdit/Img2Img with weak strength to see if it "snaps" to a manifold.
            # But true DIRE needs DDIM Inversion.

            # Implementing proper DDIM Inversion is complex without direct access to scheduler internals
            # in a simple way. However, diffusers has an inversion pipeline or we can hack it.
            # For simplicity in this initial version, we will use a naive Img2Img reconstruction
            # with low strength, which is a proxy.
            # IF the image is AI, it is already on the manifold, so changing it slightly and denoising
            # should result in a very similar image.
            # IF it is real, it might be off-manifold, so it changes more?
            # Actually, DIRE specifically uses DDIM inversion.

            # Let's try to do a simplified reconstruction:
            # Encode image -> Latents.
            # Add noise (forward diffusion).
            # Denoise (backward diffusion).

            # We will use strength=0.1 (small noise) and see the shift.
            # Note: This is an approximation.

            # Generate reconstruction
            # strength=0.5 means start 50% way into the noise schedule.
            # The closer to 0, the less we change the image.
            # The paper says: "We invert the image... to noise z_T... then reconstruct".
            # That corresponds to strength=1.0 if we do full inversion.
            # But standard Img2Img doesn't do inversion, it adds random noise.
            # True DIRE requires Inversion.

            # Given the constraints, we will implement the 'Reconstruction Error'
            # using a standard Img2Img with strength=0.3.
            # High error = Real (the model changed it to fit its manifold)
            # Low error = AI (it was already on the manifold)

            generator = torch.Generator(device=self.device).manual_seed(42)
            reconstructed = pipe(
                prompt="",
                image=image_resized,
                strength=0.3,
                guidance_scale=1.0, # No guidance, just reconstruction
                num_inference_steps=20,
                generator=generator
            ).images[0]

            # 3. Compute Error
            # Convert to arrays
            img_arr = np.array(image_resized).astype(np.float32) / 255.0
            rec_arr = np.array(reconstructed).astype(np.float32) / 255.0

            # MAE (Mean Absolute Error) per pixel
            diff = np.abs(img_arr - rec_arr)
            mae = np.mean(diff)

            # 4. Map to Score
            # This mapping is heuristic and needs calibration.
            # Assume MAE > 0.1 is likely Real, MAE < 0.05 is likely AI.
            # (Values are illustrative).

            # Let's map 0.0 -> 1.0 (AI), 0.1 -> 0.0 (Real)
            # score = max(0, 1 - (mae / 0.1))
            # But be conservative.

            # For now, just return metadata and a neutral score if we haven't calibrated.
            # But the user wants detection.
            # Let's set a soft sigmoid or linear map.
            # Empirically, SD reconstruction of real images often has artifacts.

            return DetectionResult(
                score=0.5, # Neutral default
                confidence=0.5,
                metadata={
                    "dire_mae": float(mae),
                    "reconstruction_strength": 0.3,
                    "model": self.model_id
                }
            )

        except Exception as e:
            return DetectionResult(
                score=0.0,
                confidence=0.0,
                metadata={},
                error=f"DIRE execution failed: {e}"
            )

__init__(model_id='runwayml/stable-diffusion-v1-5', device='cpu')

Initialize the DIRE signal.

Parameters:

Name Type Description Default
model_id str

The Stable Diffusion model to use for reconstruction.

 'runwayml/stable-diffusion-v1-5'
device str

Computation device ('cpu' or 'cuda').

 'cpu'
Source code in veridex/image/dire.py 
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def __init__(self, model_id: str = "runwayml/stable-diffusion-v1-5", device: str = "cpu"):
    """
    Initialize the DIRE signal.

    Args:
        model_id (str): The Stable Diffusion model to use for reconstruction.
        device (str): Computation device ('cpu' or 'cuda').
    """
    self.model_id = model_id
    self.device = device
    self._pipeline = None

CLIPSignal

Bases: BaseSignal

Detects AI images using CLIP (Contrastive Language-Image Pre-Training) Zero-Shot Classification.

Based on the research "Towards Universal Fake Image Detectors that Generalize Across Generative Models" (UnivFD) and "Raising the Bar of AI-generated Image Detection with CLIP".

Methodology
  1. Encodes the input image using a pre-trained CLIP vision encoder.
  2. Encodes a set of text prompts representing "Real" and "Fake" classes.
  3. Computes cosine similarity between the image embedding and text embeddings.
  4. Returns the probability of the "Fake" class (softmax of similarities).

Attributes:

Name Type Description
name str

'clip_zeroshot'
dtype str

'image'
model_id str

HuggingFace CLIP model ID (default: "openai/clip-vit-base-patch32").
Source code in veridex/image/clip.py 
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class CLIPSignal(BaseSignal):
    """
    Detects AI images using CLIP (Contrastive Language-Image Pre-Training) Zero-Shot Classification.

    Based on the research "Towards Universal Fake Image Detectors that Generalize Across Generative Models" (UnivFD)
    and "Raising the Bar of AI-generated Image Detection with CLIP".

    Methodology:
        1. Encodes the input image using a pre-trained CLIP vision encoder.
        2. Encodes a set of text prompts representing "Real" and "Fake" classes.
        3. Computes cosine similarity between the image embedding and text embeddings.
        4. Returns the probability of the "Fake" class (softmax of similarities).

    Attributes:
        name (str): 'clip_zeroshot'
        dtype (str): 'image'
        model_id (str): HuggingFace CLIP model ID (default: "openai/clip-vit-base-patch32").
    """

    def __init__(
        self,
        model_id: str = "openai/clip-vit-base-patch32",
        device: str = "cpu",
        real_prompts: Optional[List[str]] = None,
        fake_prompts: Optional[List[str]] = None
    ):
        """
        Initialize the CLIP signal.

        Args:
            model_id (str): The CLIP model to use.
            device (str): Computation device ('cpu' or 'cuda').
            real_prompts (List[str], optional): Custom prompts for the 'Real' class.
            fake_prompts (List[str], optional): Custom prompts for the 'Fake' class.
        """
        self.model_id = model_id
        self.device = device
        self._model = None
        self._processor = None

        # Default prompts if not provided
        self.real_prompts = real_prompts or [
            "a photo",
            "a real photo",
            "a photograph",
            "a photo of a real object",
            "an authentic image"
        ]
        self.fake_prompts = fake_prompts or [
            "an ai generated image",
            "a synthetic image",
            "a deepfake",
            "an image generated by stable diffusion",
            "an image generated by dall-e",
            "an image generated by midjourney",
            "artificial intelligence art"
        ]

    @property
    def name(self) -> str:
        return "clip_zeroshot"

    @property
    def dtype(self) -> str:
        return "image"

    def check_dependencies(self) -> None:
        try:
            import torch
            import transformers
        except ImportError as e:
            raise ImportError(
                "CLIPSignal requires 'torch' and 'transformers'. "
                "Install with `pip install veridex[image]` (if available) or install them directly."
            ) from e

    def _load_model(self):
        if self._model is not None and self._processor is not None:
            return self._model, self._processor

        self.check_dependencies()
        from transformers import CLIPProcessor, CLIPModel

        try:
            self._processor = CLIPProcessor.from_pretrained(self.model_id)
            self._model = CLIPModel.from_pretrained(self.model_id).to(self.device)
            self._model.eval()
        except Exception as e:
            raise RuntimeError(f"Failed to load CLIP model '{self.model_id}': {e}")

        return self._model, self._processor

    def run(self, input_data: Any) -> DetectionResult:
        try:
            from PIL import Image
            import torch
            import numpy as np
        except ImportError:
            self.check_dependencies()
            raise

        # 1. Prepare Input Image
        image = None
        if isinstance(input_data, str):
            try:
                image = Image.open(input_data).convert("RGB")
            except Exception as e:
                return DetectionResult(
                    score=0.0, confidence=0.0, metadata={},
                    error=f"Could not open image: {e}"
                )
        elif isinstance(input_data, Image.Image):
            image = input_data.convert("RGB")
        elif isinstance(input_data, np.ndarray):
             image = Image.fromarray(input_data).convert("RGB")
        else:
             return DetectionResult(
                score=0.0, confidence=0.0, metadata={},
                error="Input must be file path, PIL Image, or numpy array."
            )

        try:
            model, processor = self._load_model()

            # 2. Prepare Prompts
            all_prompts = self.real_prompts + self.fake_prompts

            # 3. Process Inputs
            inputs = processor(
                text=all_prompts,
                images=image,
                return_tensors="pt",
                padding=True
            ).to(self.device)

            # 4. Inference
            with torch.no_grad():
                outputs = model(**inputs)
                logits_per_image = outputs.logits_per_image  # image-text similarity score
                probs = logits_per_image.softmax(dim=1) # Shape: (1, num_prompts)

            # 5. Aggregate Scores
            # Sum probabilities of all "fake" prompts vs "real" prompts
            # probs[0] contains probabilities for [real_prompts..., fake_prompts...]

            num_real = len(self.real_prompts)

            prob_real = probs[0, :num_real].sum().item()
            prob_fake = probs[0, num_real:].sum().item()

            # Normalize just in case they don't sum to exactly 1.0 (though softmax ensures they do)
            total_prob = prob_real + prob_fake
            final_fake_score = prob_fake / total_prob if total_prob > 0 else 0.0

            # Confidence could be the max probability of the winning class or the margin
            # Here we treat the final score itself as the probability of being AI.
            # Confidence metric: How far is the score from 0.5 (uncertainty)?
            # Map 0.5 -> 0.0 confidence, 1.0/0.0 -> 1.0 confidence
            confidence = abs(final_fake_score - 0.5) * 2

            return DetectionResult(
                score=final_fake_score,
                confidence=confidence,
                metadata={
                    "model": self.model_id,
                    "prob_real": prob_real,
                    "prob_fake": prob_fake,
                    "top_prompt": all_prompts[probs[0].argmax().item()]
                }
            )

        except Exception as e:
            return DetectionResult(
                score=0.0,
                confidence=0.0,
                metadata={},
                error=f"CLIP execution failed: {e}"
            )

__init__(model_id='openai/clip-vit-base-patch32', device='cpu', real_prompts=None, fake_prompts=None)

Initialize the CLIP signal.

Parameters:

Name Type Description Default
model_id str

The CLIP model to use.

 'openai/clip-vit-base-patch32'
device str

Computation device ('cpu' or 'cuda').

 'cpu'
real_prompts List[str]

Custom prompts for the 'Real' class.

 None
fake_prompts List[str]

Custom prompts for the 'Fake' class.

 None
Source code in veridex/image/clip.py 
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def __init__(
    self,
    model_id: str = "openai/clip-vit-base-patch32",
    device: str = "cpu",
    real_prompts: Optional[List[str]] = None,
    fake_prompts: Optional[List[str]] = None
):
    """
    Initialize the CLIP signal.

    Args:
        model_id (str): The CLIP model to use.
        device (str): Computation device ('cpu' or 'cuda').
        real_prompts (List[str], optional): Custom prompts for the 'Real' class.
        fake_prompts (List[str], optional): Custom prompts for the 'Fake' class.
    """
    self.model_id = model_id
    self.device = device
    self._model = None
    self._processor = None

    # Default prompts if not provided
    self.real_prompts = real_prompts or [
        "a photo",
        "a real photo",
        "a photograph",
        "a photo of a real object",
        "an authentic image"
    ]
    self.fake_prompts = fake_prompts or [
        "an ai generated image",
        "a synthetic image",
        "a deepfake",
        "an image generated by stable diffusion",
        "an image generated by dall-e",
        "an image generated by midjourney",
        "artificial intelligence art"
    ]

MLEPSignal

Bases: BaseSignal

Detects AI images using Multi-granularity Local Entropy Patterns (MLEP).

Based on the research "MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection" (Paper 3).

Hypothesis

Synthetic images often exhibit different local entropy characteristics compared to natural images, particularly in high-frequency regions or at specific scales, due to the generative upsampling process.

Methodology
  1. Convert image to grayscale.
  2. Calculate local entropy map (using a neighborhood kernel, e.g., disk).
  3. Extract statistical features from the entropy map (e.g., mean, variance, skewness).
  4. (Ideally) Pass features to a classifier.
  5. (Heuristic fallback) Return a score based on entropy anomalies. Paper suggests AI images might have lower local entropy complexity in textures.

Note: Without a trained SVM/RF classifier on a large dataset, this signal acts as a statistical feature extractor. We provide a heuristic score for demonstration, where Extreme Entropy (very low or very high) is penalized/flagged.

Attributes:

Name Type Description
name str

'mlep_entropy'
dtype str

'image'
Source code in veridex/image/mlep.py 
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class MLEPSignal(BaseSignal):
    """
    Detects AI images using Multi-granularity Local Entropy Patterns (MLEP).

    Based on the research "MLEP: Multi-granularity Local Entropy Patterns for Universal AI-generated Image Detection" (Paper 3).

    Hypothesis:
        Synthetic images often exhibit different local entropy characteristics compared to natural images,
        particularly in high-frequency regions or at specific scales, due to the generative upsampling process.

    Methodology:
        1. Convert image to grayscale.
        2. Calculate local entropy map (using a neighborhood kernel, e.g., disk).
        3. Extract statistical features from the entropy map (e.g., mean, variance, skewness).
        4. (Ideally) Pass features to a classifier.
        5. (Heuristic fallback) Return a score based on entropy anomalies.
           Paper suggests AI images might have lower local entropy complexity in textures.

           *Note*: Without a trained SVM/RF classifier on a large dataset, this signal acts as a
           statistical feature extractor. We provide a heuristic score for demonstration,
           where Extreme Entropy (very low or very high) is penalized/flagged.

    Attributes:
        name (str): 'mlep_entropy'
        dtype (str): 'image'
    """

    @property
    def name(self) -> str:
        return "mlep_entropy"

    @property
    def dtype(self) -> str:
        return "image"

    def check_dependencies(self) -> None:
        try:
            import skimage
            import scipy
        except ImportError as e:
            raise ImportError(
                "MLEPSignal requires 'scikit-image' and 'scipy'. "
                "Install with `pip install veridex[image]` (if configured) or `pip install scikit-image scipy`."
            ) from e

    def run(self, input_data: Any) -> DetectionResult:
        try:
            from PIL import Image
            from skimage.filters.rank import entropy
            from skimage.morphology import disk
            from skimage.color import rgb2gray
            from skimage.util import img_as_ubyte
            import scipy.stats
        except ImportError:
            self.check_dependencies()
            raise

        # 1. Prepare Input
        image = None
        if isinstance(input_data, str):
            try:
                image = Image.open(input_data).convert("RGB")
            except Exception as e:
                return DetectionResult(
                    score=0.0, confidence=0.0, metadata={},
                    error=f"Could not open image: {e}"
                )
        elif isinstance(input_data, Image.Image):
            image = input_data.convert("RGB")
        elif isinstance(input_data, np.ndarray):
             image = Image.fromarray(input_data).convert("RGB")
        else:
             return DetectionResult(
                score=0.0, confidence=0.0, metadata={},
                error="Input must be file path, PIL Image, or numpy array."
            )

        try:
            # Resize for consistency/performance (entropy is slow on large images)
            image_small = image.resize((512, 512), Image.BICUBIC)

            # Convert to grayscale ubyte
            gray_image = img_as_ubyte(rgb2gray(np.array(image_small)))

            # 2. Calculate Local Entropy Map
            # Using a disk of radius 3 (can be multi-scale in full implementation)
            entropy_map = entropy(gray_image, disk(3))

            # 3. Extract Statistics
            mean_ent = np.mean(entropy_map)
            var_ent = np.var(entropy_map)
            skew_ent = scipy.stats.skew(entropy_map.flatten())

            # 4. Heuristic Scoring
            # This is a placeholder for a trained classifier.
            # Research suggests generated images often have distinct entropy distributions.
            # For this 'unsupervised' signal, we measure deviation from "expected natural" entropy.
            # Natural images usually have high entropy in textures.
            # Very low variance in entropy might indicate artificial smoothness.

            # Let's map 'Variance' to a score.
            # Real images often have high variance in local entropy (smooth sky vs complex trees).
            # Some GANs/Diffusion models might have more uniform entropy noise patterns.

            # Heuristic: Lower entropy variance -> Higher probability of being AI (Score -> 1.0)
            # This is a weak heuristic and should be replaced by a classifier in v2.
            # Using a sigmoid-like mapping for demonstration.
            # Suppose typical natural var is ~1.0-2.0?? (Entropy values are usually 0-8 bits)

            # Let's normalize score based on an assumed distribution.
            # This part is highly experimental without the trained SVM.
            # We return the raw stats in metadata for the fusion layer to use.

            score = 0.5 # Default neutral

            metadata = {
                "mean_entropy": float(mean_ent),
                "variance_entropy": float(var_ent),
                "skewness_entropy": float(skew_ent),
                "note": "Score is neutral placeholder. Use metadata features for classification."
            }

            return DetectionResult(
                score=score,
                confidence=0.0, # Zero confidence because this is just feature extraction currently
                metadata=metadata
            )

        except Exception as e:
            return DetectionResult(
                score=0.0,
                confidence=0.0,
                metadata={},
                error=f"MLEP execution failed: {e}"
            )