CHAPTER 04

Watermarking Technologies

Master invisible watermark embedding and extraction for content protection

Introduction to Digital Watermarking

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.

Watermarking Requirements

DCT-Based Watermarking

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);
            

DWT-Based Watermarking

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 Watermarking

Spread spectrum techniques distribute watermark across many frequency components for security and robustness.

โ„น๏ธ Spread Spectrum Advantage
By spreading the watermark across many coefficients, the technique is robust to localized attacks and difficult to remove without degrading image quality significantly.

LSB Steganography

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

Video watermarking extends image techniques to temporal sequences:

Audio Watermarking

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);
    }
}
            

Watermark Attacks and Robustness

Watermarks must survive both unintentional and malicious modifications:

Common Attacks

Blind vs. Non-Blind Detection

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);
    }
}
            

Benchmarking Watermark Robustness

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}ยฐ`,
                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;
    }
}
            

Integration with C2PA

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
        };
    }
}
            

๐Ÿ“Œ Key Takeaways

๐Ÿ“ Review Questions

  1. What are the five key requirements for robust watermarking?
  2. Why is DCT-based watermarking particularly robust to JPEG compression?
  3. Explain the trade-off between LSB steganography and DCT watermarking.
  4. How does spread spectrum watermarking improve security?
  5. What is the difference between blind and non-blind watermark detection?
  6. Describe three types of attacks watermarks must survive.
  7. Why is video watermarking more complex than image watermarking?
  8. How do watermarks complement C2PA metadata in content authentication?

ๅผ˜็›Šไบบ้–“ (ํ™์ต์ธ๊ฐ„) ยท Benefit All Humanity