Inside DotScramble: Metadata Spoofing, Custom EXIF Control, and Advanced Face/Eye Blur

A deep dive into how DotScramble protects your privacy — from injecting plausible-but-fake EXIF metadata to detecting and blurring faces and eyes with OpenCV Haar Cascades.

TL;DR — DotScramble is an open-source desktop app that protects image privacy through two main pillars: metadata obfuscation (stripping or spoofing EXIF data so your camera, GPS, and timestamp can't be traced back to you) and visual redaction (automatically detecting and blurring faces, eyes, license plates, and text). This post digs into the code behind both systems.

Why Metadata Is the Hidden Threat

When you take a photo, the file doesn't just contain pixels. Hidden inside is a block of EXIF metadata that silently stores:

Field	Example Value	Risk
`GPS Latitude/Longitude`	`30.0444° N, 31.2357° E`	Reveals your exact location
`Camera Make/Model`	`Apple iPhone 15 Pro`	Fingerprints your device
`DateTimeOriginal`	`2026:06:21 14:32:11`	Timestamps your movements
`Software`	`iOS 17.5`	Exposes OS/platform

Most people strip this metadata before sharing photos — but stripping alone can look suspicious. A completely blank EXIF block on a modern smartphone image is a red flag to any tracking algorithm. DotScramble takes a different approach: instead of silent stripping, it injects plausible-but-fake disinformation.

Part 1: The Metadata Spoofing Engine

The core of the system lives in core/metadata_spoofer.py.

The Camera Database — Intentionally Vintage

FAKE_CAMERAS: list[tuple[str, str, str]] = [
    ("Nokia",     "3310",              "Nokia Imaging 1.0"),
    ("Motorola",  "RAZR V3",           "Motorola Camera 1.2"),
    ("Samsung",   "SCH-U340",          "Samsung Digimax 2.1"),
    ("Casio",     "QV-10A",            "Casio Digital Camera"),
    ("Polaroid",  "PDC 640",           "Polaroid Software 1.1"),
    ("Kodak",     "DC40",              "Kodak EasyShare 3.0"),
    # ... and more
]

The list is intentionally filled with vintage and implausible devices — a Nokia 3310 doesn't have a camera at all, which is exactly the point. Trackers fingerprint devices by cross-referencing camera model with image resolution, color profile, and noise patterns. Injecting a Nokia 3310 signature into a modern 12MP photo creates an irreconcilable contradiction that defeats fingerprinting.

GPS Presets — Middle of Nowhere

GPS_PRESETS: dict[str, tuple[float, float]] = {
    "pacific":    (   4.2234,  -157.4521),  # Middle of Pacific Ocean
    "antarctica": ( -89.3312,    12.0000),  # Antarctica
    "arctic":     (  89.1122,  -178.4343),  # Arctic Ocean
    "sahara":     (  23.4122,    10.9988),  # Remote Sahara (no cell towers)
    "amazon":     (  -5.3421,   -63.2231),  # Deep Amazon basin
}

These aren't random — each preset was chosen because it's a location with no plausible human population or cell infrastructure, making it impossible to cross-reference the fake GPS against telecom tower data.

A small jitter is added to each preset on every run so the same preset never produces the exact same coordinates twice:

def _pick_gps(preset, custom, jitter=True):
    coords = GPS_PRESETS.get(preset, GPS_PRESETS["pacific"])
    if jitter:
        lat = coords[0] + random.uniform(-0.08, 0.08)
        lon = coords[1] + random.uniform(-0.08, 0.08)
        return (round(lat, 4), round(lon, 4))
    return coords

Converting GPS to EXIF Rationals

EXIF GPS doesn't store decimal degrees directly — it uses a DMS (Degrees, Minutes, Seconds) format with rational numbers (numerator/denominator pairs). Here's how DotScramble handles the conversion:

def _dms_rational(value: float) -> tuple[tuple[int, int], ...]:
    """Convert decimal degrees → (deg, min, sec) as EXIF rational tuples."""
    abs_v = abs(value)
    deg   = int(abs_v)
    m_f   = (abs_v - deg) * 60
    mins  = int(m_f)
    secs  = round((m_f - mins) * 60 * 10_000)
    return ((deg, 1), (mins, 1), (secs, 10_000))

The result gets packaged into a full GPS IFD:

def _build_gps_ifd(lat: float, lon: float) -> dict:
    return {
        piexif.GPSIFD.GPSLatitudeRef:  b"S" if lat < 0 else b"N",
        piexif.GPSIFD.GPSLatitude:     _dms_rational(lat),
        piexif.GPSIFD.GPSLongitudeRef: b"W" if lon < 0 else b"E",
        piexif.GPSIFD.GPSLongitude:    _dms_rational(lon),
        piexif.GPSIFD.GPSAltitude:     (0, 1),
        piexif.GPSIFD.GPSMapDatum:     b"WGS-84",
    }

Spoof Profiles — Opinionated Presets

Rather than exposing raw settings, DotScramble exposes three high-level profiles:

PROFILES = {
    "ghost": {
        # Maximum obfuscation — Nokia 3310 in Antarctica, year 2000
        "gps_preset":         "antarctica",
        "camera":             "Nokia 3310",
        "fake_datetime_mode": "epoch",
        "keep_copyright":     False,
    },
    "troll": {
        # Plausibly wrong — recent vintage camera, random ocean
        "gps_preset":         "pacific",
        "camera":             "random",
        "fake_datetime_mode": "recent",
        "keep_copyright":     False,
    },
    "artist": {
        # For photographers — strips location+device, preserves copyright
        "gps_preset":         "atlantic",
        "camera":             "random",
        "fake_datetime_mode": "random",
        "keep_copyright":     True,
    },
}

Usage is dead simple from Python:

from core.metadata_spoofer import spoof

result = spoof("photo.jpg", profile="ghost")
print(result)
# {
#   "format": "JPEG",
#   "camera": "Nokia 3310",
#   "gps": {"lat": -89.2891, "lon": 12.0412},
#   "datetime": "2000:01:01 00:00:00",
#   ...
# }

Or from the CLI:

dotscramble-spoof photo.jpg --profile ghost
dotscramble-spoof photo.jpg --gps-preset pacific --camera "Kodak DC40"
dotscramble-spoof photo.jpg --gps-custom 23.4 -54.2 --keep-copyright
dotscramble-spoof photo.jpg --dry-run --json

JPEG vs PNG — Different Metadata Formats

JPEG and PNG handle metadata completely differently:

JPEG uses binary EXIF chunks (IFD tables). DotScramble builds these using piexif:

exif_bytes = piexif.dump({
    "0th":  zeroth_ifd,   # Camera make, model, software, datetime
    "Exif": exif_ifd,     # DateTimeOriginal, ISO, aperture, shutter
    "GPS":  gps_ifd,      # Lat/lon in DMS rational format
})
img.save(output_path, "JPEG", exif=exif_bytes, quality=95)

PNG uses simple text chunks (key-value pairs). No GPS support in the standard:

pnginfo = PngImagePlugin.PngInfo()
pnginfo.add_text("Software",      software)
pnginfo.add_text("Author",        make)
pnginfo.add_text("Creation Time", png_dt)
pnginfo.add_text("date:create",   iso_dt)
pnginfo.add_text("date:modify",   iso_dt)
img.save(output_path, "PNG", pnginfo=pnginfo)

Design Note: PNG's text chunks are completely human-readable with any hex editor. DotScramble still populates them because many social platforms and reverse image search engines parse them. Injecting noise is better than leaving them empty.

Part 2: Custom Metadata Control — Per-Field EXIF Surgery

The spoofing profiles are great for quick use, but power users need surgical control. That's what the Custom Metadata Dialog provides — a per-field control panel where every EXIF tag can independently be set to one of four actions:

✅ Keep   — preserve the original value exactly
🗑 Strip  — remove this field entirely  
🎲 Spoof  — replace with a random plausible fake
✏️ Custom — enter your own specific value

The `spoof_custom()` Function

This is the engine behind the dialog. It takes a field_actions dict and applies each action independently:

def spoof_custom(
    input_path: str,
    output_path: str | None = None,
    *,
    field_actions: dict,
) -> dict:
    """
    Apply per-field EXIF actions to a JPEG or PNG.

    field_actions keys: gps, make, model, software, datetime, copyright, exposure
    Values: "keep" | "strip" | "spoof" | {"value": str}
            (gps uses {"lat": .., "lon": ..} for custom)
    """

The resolver function handles all four action types elegantly:

def _resolve_text(key: str, fake_fn) -> str | None:
    action = field_actions.get(key, "keep")
    if action == "strip":             return None
    if action == "keep":              return current.get(key)
    if action == "spoof":             return fake_fn()
    if isinstance(action, dict):      return action.get("value", "")
    return current.get(key)

# Applied per-field:
r_make     = _resolve_text("make",     lambda: make)
r_model    = _resolve_text("model",    lambda: model)
r_software = _resolve_text("software", lambda: software)
r_datetime = _resolve_text("datetime", lambda: _fake_timestamp("random"))

Reading Existing EXIF to Pre-fill the UI

Before showing the dialog, DotScramble reads the current EXIF so the user can see what's actually in the file:

def read_exif_fields(image_path: str) -> dict:
    """Read current EXIF and return structured dict for pre-filling the dialog."""
    result = {k: None for k in
              ("gps", "make", "model", "software", "datetime", "copyright", "exposure")}

    exif_dict = piexif.load(image_path)
    zeroth = exif_dict.get("0th", {})
    exif   = exif_dict.get("Exif", {})
    gps    = exif_dict.get("GPS", {})

    result["make"]     = _decode(zeroth, piexif.ImageIFD.Make)
    result["model"]    = _decode(zeroth, piexif.ImageIFD.Model)
    result["datetime"] = _decode(zeroth, piexif.ImageIFD.DateTime)
    # ... GPS DMS → decimal conversion, exposure parsing, etc.
    return result

The GPS conversion from DMS rational back to decimal is the tricky part:

def _dms_to_decimal(dms_tuple, ref: bytes) -> float | None:
    d = _rational(dms_tuple[0]) or 0
    m = _rational(dms_tuple[1]) or 0
    s = _rational(dms_tuple[2]) or 0
    val = d + m / 60 + s / 3600
    if ref in (b"S", b"W"):
        val = -val
    return round(val, 6)

Preset System for the Dialog

The Custom dialog also supports saving and loading named presets, backed by a JSON file:

# Example saved preset
{
    "gps":       "strip",
    "make":      "spoof",
    "model":     "spoof",
    "software":  "strip",
    "datetime":  {"value": "2023:01:01 12:00:00"},
    "copyright": "keep",
    "exposure":  "spoof"
}

Factory presets ship with the app (Ghost, Troll, Artist), and users can create their own. Factory presets are read-only and can't be deleted or overwritten.

Part 3: Advanced Face and Eye Blur

The visual redaction engine lives in core/image_processor.py.

The Detection Engine

DotScramble uses OpenCV's Haar Cascade classifiers — a classical machine learning approach that's fast, works offline, and has no cloud dependencies. The DetectionEngine class wraps the key classifiers:

class DetectionEngine:
    """Advanced detection algorithms"""

    @staticmethod
    def detect_faces(image):
        """Detect faces using Haar Cascade"""
        face_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
        )
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.1, 5, minSize=(30, 30))
        return faces

    @staticmethod
    def detect_eyes(image):
        """Detect eyes using Haar Cascade"""
        eye_cascade = cv2.CascadeClassifier(
            cv2.data.haarcascades + 'haarcascade_eye.xml'
        )
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        eyes = eye_cascade.detectMultiScale(gray, 1.1, 5, minSize=(20, 20))
        return eyes

The parameters that matter:

scaleFactor=1.1 — how much the image is scaled down per detection pass (1.1 = 10% reduction per pass, slower but more accurate)
minNeighbors=5 — how many overlapping detections a region needs before it's accepted (higher = fewer false positives)
minSize=(30, 30) — minimum region size in pixels (filters out noise)

Applying the Blur — The `gaussian_blur` Method

Once regions are detected, DotScramble applies effects. The gaussian_blur implementation has extensive validation to handle edge cases like faces at image borders:

@staticmethod
def gaussian_blur(image, x, y, w, h, strength):
    """Apply Gaussian blur to region with validation"""
    img_h, img_w = image.shape[:2]

    # Clip region to image boundaries (handles faces at edges)
    x_end = min(x + w, img_w)
    y_end = min(y + h, img_h)
    w = x_end - x
    h = y_end - y

    # Ensure minimum region size
    if w < 3 or h < 3:
        raise ValueError(f"Region too small: {w}x{h}")

    # GaussianBlur requires odd kernel size
    if strength % 2 == 0:
        strength += 1
    strength = max(3, strength)

    region = image[y:y_end, x:x_end]
    blurred = cv2.GaussianBlur(region, (strength, strength), 0)
    return blurred

Beyond Blur — The Full Effect Suite

Blur is just one option. DotScramble supports 7 different privacy effects that can be applied to any detected region:

Effect	Method	Best For
🌫️ Gaussian Blur	`gaussian_blur()`	Professional, natural look
🔲 Pixelation	`pixelate()`	Classic censoring style
⬛ Black Bar	`black_bar()`	Strong, unambiguous redaction
🎭 Gradient Fade	`gradient_fade()`	Artistic/subtle censoring
🔳 Mosaic	`mosaic_effect()`	Decorative tile pattern
❄️ Frosted Glass	`frosted_glass()`	Translucent glass aesthetic
🎨 Oil Paint	`oil_paint()`	Artistic painting effect

The pixelation effect is a neat double-resize trick — shrink down, then scale back up with nearest-neighbor interpolation:

@staticmethod
def pixelate(image, x, y, w, h, pixel_size):
    region = image[y:y_end, x:x_end]
    region_h, region_w = region.shape[:2]
    pixel_size = max(1, pixel_size)

    # Shrink → enlarge = pixelation
    temp_h = max(1, region_h // pixel_size)
    temp_w = max(1, region_w // pixel_size)
    temp = cv2.resize(region, (temp_w, temp_h), interpolation=cv2.INTER_LINEAR)
    pixelated = cv2.resize(temp, (region_w, region_h), interpolation=cv2.INTER_NEAREST)
    return pixelated

The frosted glass effect combines PIL and OpenCV:

@staticmethod
def frosted_glass(image, x, y, w, h, strength=15):
    region = image[y:y_end, x:x_end]

    # Convert to PIL for advanced filtering
    pil_region = Image.fromarray(cv2.cvtColor(region, cv2.COLOR_BGR2RGB))

    blurred  = pil_region.filter(ImageFilter.GaussianBlur(strength))
    enhanced = ImageEnhance.Brightness(blurred).enhance(1.1)
    final    = enhanced.filter(ImageFilter.EDGE_ENHANCE)

    # Back to OpenCV format
    return cv2.cvtColor(np.array(final), cv2.COLOR_RGB2BGR)

Opacity Blending — Partial Redaction

All effects support an opacity parameter that blends the processed region with the original:

@staticmethod
def apply_opacity(original, processed, opacity):
    """Blend processed region with original based on opacity"""
    if original.shape != processed.shape:
        processed = cv2.resize(processed, (original.shape[1], original.shape[0]))

    alpha = opacity / 100.0
    return cv2.addWeighted(processed, alpha, original, 1 - alpha, 0)

This is useful for subtle watermark-style redaction (e.g., 50% opacity blur) vs. hard censoring (100%).

The Full Workflow

📁 Load Image
    │
    ▼
🎯 Select Detection Mode
    ├── Face Detection (Haar Cascade)
    ├── Eye Detection (Haar Cascade)
    ├── Full Body Detection
    ├── License Plate (contour analysis)
    ├── Text Detection (Tesseract OCR)
    ├── Manual Selection (draw regions)
    └── Full Image
    │
    ▼
🎨 Choose Effect + Strength + Opacity
    │
    ▼
🛡️  Apply Metadata Control
    ├── Quick Profile (ghost / troll / artist)
    └── Custom Per-Field (keep/strip/spoof/custom)
    │
    ▼
💾 Save / Batch Export

Why Haar Cascades Instead of Deep Learning?

I get this question a lot. DotScramble intentionally chose Haar Cascades over modern deep learning detectors like YOLO or MediaPipe face mesh. The reasons:

No cloud, no internet — Haar Cascades run entirely offline. Privacy tools should never phone home.
Zero model download on first run — the XML classifiers are bundled with OpenCV. No 100MB model download.
Fast on CPU — users don't need a GPU. The app runs fine on a 5-year-old laptop.
Good enough for the use case — the goal is privacy, not 99.9% recall. If a face is missed, Manual Selection is always available as fallback.

That said, AI-powered tracking (YOLOv8 or similar) is on the roadmap for v1.4.0 to improve detection of faces at angles, in poor lighting, or partially occluded.

Getting Started

# Clone
git clone https://github.com/kareem2099/DotScramble.git
cd DotScramble

# Install dependencies
pip install -r requirements.txt
# pip install opencv-python numpy Pillow pytesseract piexif PySide6

# Optional: Tesseract for text detection
sudo apt install tesseract-ocr

# Run
python src/main.py

Or grab the standalone Linux executable — no Python required — from one of these:

Platform	Link
🐙 GitHub Releases	Latest Release
🖥️ OpenDesktop / Pling	opendesktop.org/p/2362477

Conclusion

DotScramble's metadata system takes a "active disinformation" approach rather than passive stripping — the result is metadata that looks legitimate while being completely meaningless. Combined with the visual redaction engine, it provides a comprehensive privacy toolkit that works entirely offline.

The code is Apache 2.0 licensed and open to contributions. If you're interested in adding AI tracking, GPU acceleration, or a web interface — pull requests are very welcome.

GitHub: github.com/kareem2099/DotScramble

OpenDesktop: opendesktop.org/p/2362477

Made with ❤️ for privacy protection

Inside DotScramble: Metadata Spoofing, Custom EXIF Control, and Advanced Face/Eye Blur

A deep dive into how DotScramble protects your privacy — from injecting plausible-but-fake EXIF metadata to detecting and blurring faces and eyes with OpenCV Haar Cascades.

Why Metadata Is the Hidden Threat

Part 1: The Metadata Spoofing Engine

The Camera Database — Intentionally Vintage

GPS Presets — Middle of Nowhere

Converting GPS to EXIF Rationals

Spoof Profiles — Opinionated Presets

JPEG vs PNG — Different Metadata Formats

Part 2: Custom Metadata Control — Per-Field EXIF Surgery

The `spoof_custom()` Function

Reading Existing EXIF to Pre-fill the UI

Preset System for the Dialog

Part 3: Advanced Face and Eye Blur

The Detection Engine

Applying the Blur — The `gaussian_blur` Method

Beyond Blur — The Full Effect Suite

Opacity Blending — Partial Redaction

The Full Workflow

Why Haar Cascades Instead of Deep Learning?

Getting Started

Conclusion

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Inside DotScramble: Metadata Spoofing, Custom EXIF Control, and Advanced Face/Eye Blur

A deep dive into how DotScramble protects your privacy — from injecting plausible-but-fake EXIF metadata to detecting and blurring faces and eyes with OpenCV Haar Cascades.

Why Metadata Is the Hidden Threat

Part 1: The Metadata Spoofing Engine

The Camera Database — Intentionally Vintage

GPS Presets — Middle of Nowhere

Converting GPS to EXIF Rationals

Spoof Profiles — Opinionated Presets

JPEG vs PNG — Different Metadata Formats

Part 2: Custom Metadata Control — Per-Field EXIF Surgery

The spoof_custom() Function

Reading Existing EXIF to Pre-fill the UI

Preset System for the Dialog

Part 3: Advanced Face and Eye Blur

The Detection Engine

Applying the Blur — The gaussian_blur Method

Beyond Blur — The Full Effect Suite

Opacity Blending — Partial Redaction

The Full Workflow

Why Haar Cascades Instead of Deep Learning?

Getting Started

Conclusion

Tags

Author

Stats

Published

You Might Also Like

Some friends wanted to see how I use DigitalOcean. So I built them the smallest real app I could.

The LLM Visibility Tools Cost $79/Month. Mine is Open Source.

On programming languages, targets, and platforms

I almost added an em-dash remover to my LLM library. Then I tested whether local models even produce em-dashes.

My eval harness paid for itself on the first run: 0.57 0.96, two bugs no unit test could catch

Never lose a training run again: a checkpoint-and-resume playbook for ephemeral GPUs

The `spoof_custom()` Function

Applying the Blur — The `gaussian_blur` Method