Master invisible watermark embedding and extraction for content protection
Digital watermarking embeds information directly into content in a way that survives common transformations. Unlike metadata that can be stripped, watermarks are an integral part of the content itself. The WIA-AI-017 standard specifies watermarking requirements for AI content authentication.
Discrete Cosine Transform watermarking modifies frequency coefficients for robustness to JPEG compression.
// Example: DCT Watermarking Implementation
import Jimp from 'jimp';
class DCTWatermarker {
constructor(strength = 10) {
this.strength = strength;
this.blockSize = 8;
}
async embed(imagePath, watermarkBits) {
const image = await Jimp.read(imagePath);
const { width, height } = image.bitmap;
// Process 8x8 blocks
for (let y = 0; y < height - this.blockSize; y += this.blockSize) {
for (let x = 0; x < width - this.blockSize; x += this.blockSize) {
const block = this.extractBlock(image, x, y);
const dct = this.computeDCT(block);
// Embed watermark bit in mid-frequency coefficient
const bitIndex = ((y / this.blockSize) * (width / this.blockSize) +
(x / this.blockSize)) % watermarkBits.length;
const bit = watermarkBits[bitIndex];
// Modify DCT coefficient
dct[3][3] = bit === 1 ?
Math.abs(dct[3][3]) + this.strength :
-Math.abs(dct[3][3]) - this.strength;
// Inverse DCT and write back
const modifiedBlock = this.inverseDCT(dct);
this.writeBlock(image, x, y, modifiedBlock);
}
}
return image;
}
async extract(imagePath, watermarkLength) {
const image = await Jimp.read(imagePath);
const { width, height } = image.bitmap;
const extractedBits = [];
for (let y = 0; y < height - this.blockSize; y += this.blockSize) {
for (let x = 0; x < width - this.blockSize; x += this.blockSize) {
const block = this.extractBlock(image, x, y);
const dct = this.computeDCT(block);
// Extract bit from mid-frequency coefficient
const bit = dct[3][3] > 0 ? 1 : 0;
extractedBits.push(bit);
if (extractedBits.length >= watermarkLength) {
return extractedBits;
}
}
}
return extractedBits;
}
extractBlock(image, startX, startY) {
const block = [];
for (let y = 0; y < this.blockSize; y++) {
const row = [];
for (let x = 0; x < this.blockSize; x++) {
const pixel = Jimp.intToRGBA(image.getPixelColor(startX + x, startY + y));
row.push(pixel.r); // Use red channel
}
block.push(row);
}
return block;
}
writeBlock(image, startX, startY, block) {
for (let y = 0; y < this.blockSize; y++) {
for (let x = 0; x < this.blockSize; x++) {
const value = Math.max(0, Math.min(255, Math.round(block[y][x])));
const color = Jimp.rgbaToInt(value, value, value, 255);
image.setPixelColor(color, startX + x, startY + y);
}
}
}
computeDCT(block) {
const N = this.blockSize;
const dct = Array(N).fill(0).map(() => Array(N).fill(0));
for (let u = 0; u < N; u++) {
for (let v = 0; v < N; v++) {
let sum = 0;
for (let x = 0; x < N; x++) {
for (let y = 0; y < N; y++) {
sum += block[y][x] *
Math.cos(Math.PI * u * (2 * x + 1) / (2 * N)) *
Math.cos(Math.PI * v * (2 * y + 1) / (2 * N));
}
}
const cu = u === 0 ? 1 / Math.sqrt(2) : 1;
const cv = v === 0 ? 1 / Math.sqrt(2) : 1;
dct[u][v] = 0.25 * cu * cv * sum;
}
}
return dct;
}
inverseDCT(dct) {
const N = this.blockSize;
const block = Array(N).fill(0).map(() => Array(N).fill(0));
for (let x = 0; x < N; x++) {
for (let y = 0; y < N; y++) {
let sum = 0;
for (let u = 0; u < N; u++) {
for (let v = 0; v < N; v++) {
const cu = u === 0 ? 1 / Math.sqrt(2) : 1;
const cv = v === 0 ? 1 / Math.sqrt(2) : 1;
sum += cu * cv * dct[u][v] *
Math.cos(Math.PI * u * (2 * x + 1) / (2 * N)) *
Math.cos(Math.PI * v * (2 * y + 1) / (2 * N));
}
}
block[y][x] = 0.25 * sum;
}
}
return block;
}
}
// Usage
const watermarker = new DCTWatermarker(15);
const watermarkData = "WIA-AI-017 Authenticated Content";
const watermarkBits = stringToBits(watermarkData);
const watermarked = await watermarker.embed('original.jpg', watermarkBits);
await watermarked.writeAsync('watermarked.jpg');
const extracted = await watermarker.extract('watermarked.jpg', watermarkBits.length);
const extractedText = bitsToString(extracted);
console.log('Extracted watermark:', extractedText);
Discrete Wavelet Transform provides better imperceptibility by modifying multi-scale representations.
// Example: DWT Watermarking
class DWTWatermarker {
constructor(level = 3) {
this.level = level;
}
async embed(imagePath, watermarkBits) {
const image = await Jimp.read(imagePath);
// Convert to YCbCr color space
const y = this.extractLuminance(image);
// Perform DWT
const coeffs = this.dwt2D(y, this.level);
// Embed in LL subband (low-frequency approximation)
const ll = coeffs.LL;
for (let i = 0; i < watermarkBits.length && i < ll.length; i++) {
for (let j = 0; j < ll[i].length && (i * ll[i].length + j) < watermarkBits.length; j++) {
const bitIndex = i * ll[i].length + j;
if (bitIndex < watermarkBits.length) {
// Quantization-based embedding
const bit = watermarkBits[bitIndex];
const delta = 20; // Quantization step
const quantized = Math.round(ll[i][j] / delta) * delta;
ll[i][j] = quantized + (bit === 1 ? delta / 2 : 0);
}
}
}
// Inverse DWT
const reconstructed = this.idwt2D(coeffs, this.level);
// Write back to image
this.writeLuminance(image, reconstructed);
return image;
}
dwt2D(matrix, level) {
// Haar wavelet transform
let current = matrix;
const coefficients = { LL: null, LH: [], HL: [], HH: [] };
for (let l = 0; l < level; l++) {
const result = this.haarTransform(current);
coefficients.LH.push(result.LH);
coefficients.HL.push(result.HL);
coefficients.HH.push(result.HH);
current = result.LL;
}
coefficients.LL = current;
return coefficients;
}
haarTransform(matrix) {
const rows = matrix.length;
const cols = matrix[0].length;
const halfRows = Math.floor(rows / 2);
const halfCols = Math.floor(cols / 2);
const LL = Array(halfRows).fill(0).map(() => Array(halfCols).fill(0));
const LH = Array(halfRows).fill(0).map(() => Array(halfCols).fill(0));
const HL = Array(halfRows).fill(0).map(() => Array(halfCols).fill(0));
const HH = Array(halfRows).fill(0).map(() => Array(halfCols).fill(0));
for (let i = 0; i < halfRows; i++) {
for (let j = 0; j < halfCols; j++) {
const a = matrix[2*i][2*j];
const b = matrix[2*i][2*j+1] || a;
const c = matrix[2*i+1] ? matrix[2*i+1][2*j] : a;
const d = matrix[2*i+1] ? matrix[2*i+1][2*j+1] || c : a;
LL[i][j] = (a + b + c + d) / 4;
LH[i][j] = (a + b - c - d) / 4;
HL[i][j] = (a - b + c - d) / 4;
HH[i][j] = (a - b - c + d) / 4;
}
}
return { LL, LH, HL, HH };
}
// Additional helper methods for extraction, IDWT, etc.
}
Spread spectrum techniques distribute watermark across many frequency components for security and robustness.
Least Significant Bit embedding offers high capacity but limited robustness:
// Example: LSB Watermarking (High Capacity, Low Robustness)
class LSBWatermarker {
async embed(imagePath, watermarkData) {
const image = await Jimp.read(imagePath);
const bits = stringToBits(watermarkData);
let bitIndex = 0;
image.scan(0, 0, image.bitmap.width, image.bitmap.height, (x, y, idx) => {
if (bitIndex < bits.length) {
// Modify LSB of red channel
const currentByte = image.bitmap.data[idx];
image.bitmap.data[idx] = (currentByte & 0xFE) | bits[bitIndex];
bitIndex++;
}
});
return image;
}
async extract(imagePath, dataLength) {
const image = await Jimp.read(imagePath);
const bits = [];
image.scan(0, 0, image.bitmap.width, image.bitmap.height, (x, y, idx) => {
if (bits.length < dataLength * 8) {
bits.push(image.bitmap.data[idx] & 1);
}
});
return bitsToString(bits);
}
}
function stringToBits(str) {
const bits = [];
for (let i = 0; i < str.length; i++) {
const code = str.charCodeAt(i);
for (let j = 7; j >= 0; j--) {
bits.push((code >> j) & 1);
}
}
return bits;
}
function bitsToString(bits) {
let str = '';
for (let i = 0; i < bits.length; i += 8) {
let byte = 0;
for (let j = 0; j < 8 && i + j < bits.length; j++) {
byte = (byte << 1) | bits[i + j];
}
str += String.fromCharCode(byte);
}
return str;
}
Video watermarking extends image techniques to temporal sequences:
Audio watermarking techniques leverage psychoacoustic models:
// Example: Echo Hiding for Audio Watermarking
class AudioWatermarker {
constructor(delay1 = 0.001, delay2 = 0.002, decay = 0.5) {
this.delay1 = delay1; // Delay for bit 0
this.delay2 = delay2; // Delay for bit 1
this.decay = decay;
}
embedBit(audioSegment, bit) {
const delay = bit === 1 ? this.delay2 : this.delay1;
const delaySamples = Math.floor(delay * audioSegment.sampleRate);
const watermarked = [...audioSegment.samples];
for (let i = delaySamples; i < watermarked.length; i++) {
watermarked[i] += this.decay * watermarked[i - delaySamples];
}
return watermarked;
}
extractBit(audioSegment) {
// Compute autocorrelation at both delays
const corr1 = this.autocorrelation(audioSegment, this.delay1);
const corr2 = this.autocorrelation(audioSegment, this.delay2);
return corr2 > corr1 ? 1 : 0;
}
autocorrelation(segment, delay) {
const delaySamples = Math.floor(delay * segment.sampleRate);
let sum = 0;
for (let i = delaySamples; i < segment.samples.length; i++) {
sum += segment.samples[i] * segment.samples[i - delaySamples];
}
return sum / (segment.samples.length - delaySamples);
}
}
Watermarks must survive both unintentional and malicious modifications:
Blind watermark detection doesn't require the original content:
// Example: Blind Watermark Detection
class BlindWatermarkDetector {
async detect(watermarkedImage, expectedWatermark) {
// Extract watermark without original image
const extracted = await this.extractWatermark(watermarkedImage);
// Compute correlation with expected watermark
const correlation = this.correlate(extracted, expectedWatermark);
// Threshold-based detection
const threshold = 0.7;
const detected = correlation > threshold;
return {
detected: detected,
correlation: correlation.toFixed(3),
confidence: (correlation * 100).toFixed(1) + '%'
};
}
correlate(signal1, signal2) {
if (signal1.length !== signal2.length) {
throw new Error('Signals must have same length');
}
let numerator = 0;
let sum1sq = 0;
let sum2sq = 0;
for (let i = 0; i < signal1.length; i++) {
numerator += signal1[i] * signal2[i];
sum1sq += signal1[i] * signal1[i];
sum2sq += signal2[i] * signal2[i];
}
return numerator / Math.sqrt(sum1sq * sum2sq);
}
async extractWatermark(image) {
// Implementation depends on embedding method
// This is a placeholder for DCT-based extraction
const dct = this.computeImageDCT(image);
return this.extractFromDCT(dct);
}
}
Systematic testing ensures watermarks meet WIA-AI-017 requirements:
// Example: Watermark Robustness Benchmark
class WatermarkBenchmark {
async runTests(watermarker, testImage, watermarkData) {
const results = [];
// Embed watermark
const watermarked = await watermarker.embed(testImage, watermarkData);
// Test 1: JPEG Compression
for (let quality = 10; quality <= 100; quality += 10) {
const compressed = await this.compressJPEG(watermarked, quality);
const extracted = await watermarker.extract(compressed);
const ber = this.bitErrorRate(watermarkData, extracted);
results.push({
test: `JPEG Q${quality}`,
ber: ber,
passed: ber < 0.1 // <10% bit error rate
});
}
// Test 2: Scaling
for (let scale of [0.5, 0.75, 1.25, 1.5, 2.0]) {
const scaled = await this.scaleImage(watermarked, scale);
const extracted = await watermarker.extract(scaled);
const ber = this.bitErrorRate(watermarkData, extracted);
results.push({
test: `Scale ${scale}x`,
ber: ber,
passed: ber < 0.1
});
}
// Test 3: Rotation
for (let angle of [-15, -10, -5, 5, 10, 15]) {
const rotated = await this.rotateImage(watermarked, angle);
const extracted = await watermarker.extract(rotated);
const ber = this.bitErrorRate(watermarkData, extracted);
results.push({
test: `Rotate ${angle}°`,
ber: ber,
passed: ber < 0.15 // Slightly higher tolerance
});
}
// Generate report
const totalTests = results.length;
const passed = results.filter(r => r.passed).length;
return {
totalTests,
passed,
failed: totalTests - passed,
passRate: ((passed / totalTests) * 100).toFixed(1) + '%',
results
};
}
bitErrorRate(original, extracted) {
let errors = 0;
const length = Math.min(original.length, extracted.length);
for (let i = 0; i < length; i++) {
if (original[i] !== extracted[i]) errors++;
}
return errors / length;
}
}
WIA-AI-017 watermarks complement C2PA metadata for comprehensive authentication:
// Example: Combined Watermark + C2PA
class AuthenticatedContent {
async createAuthenticatedContent(content, metadata) {
// Step 1: Embed watermark
const watermarkData = {
content_id: generateUUID(),
creator: metadata.creator,
timestamp: new Date().toISOString(),
ai_model: metadata.ai_model
};
const watermarked = await this.embedWatermark(content, watermarkData);
// Step 2: Create C2PA manifest
const c2paManifest = this.createC2PAManifest(metadata, watermarkData);
// Step 3: Sign content
const signature = await this.signContent(watermarked, c2paManifest);
// Step 4: Combine everything
return {
content: watermarked,
manifest: c2paManifest,
signature: signature,
watermark_embedded: true
};
}
async verifyAuthenticatedContent(content) {
// Step 1: Verify signature
const signatureValid = await this.verifySignature(content);
// Step 2: Extract and verify watermark
const watermark = await this.extractWatermark(content);
const watermarkValid = this.verifyWatermark(watermark);
// Step 3: Validate C2PA manifest
const manifestValid = await this.validateC2PAManifest(content);
return {
authenticated: signatureValid && watermarkValid && manifestValid,
signature_valid: signatureValid,
watermark_valid: watermarkValid,
manifest_valid: manifestValid,
watermark_data: watermark
};
}
}
弘益人間 (홍익인간) · Benefit All Humanity
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