NoisePrints: Forensics & Fingerprinting

• NoisePrints are unique noise-associated patterns inherent to devices and models, used for forensics, authorship verification, and robust digital authentication.
• They are extracted via denoising filters and deep learning techniques—such as Siamese CNNs—to isolate high-frequency artifacts for precise device and model attribution.
• Integrating multi-domain fusion and cryptographic watermarking, NoisePrints improve detection of digital manipulations and bolster integrity verification across varied applications.

NoisePrints represent distinctive noise-associated patterns—sometimes called "imprints" or "fingerprints"—that are intrinsic to the data generation process of devices, models, or physical systems. These patterns arise from either stochastic processes, hardware design, digital pipelines, or initialization protocols and can be leveraged for applications in forensics, authorship verification, robust identification, and integrity validation across a spectrum of domains: camera-based imagery (Cozzolino et al., 2018, Cozzolino et al., 2020), quantum devices (Martina et al., 2021), fingerprints (Joshi et al., 2021), audio (Akesbi, 2022), and generative models (Li et al., 12 Mar 2025, Goren et al., 15 Oct 2025). The notion has evolved from foundational studies of sensor pattern noise—most notably the PRNU (Photo-Response Non-Uniformity)—to encompass unified concepts integrating model-level noise, data uncertainty, and cryptographically entangled watermarking.

1. Origins and General Principles

NoisePrints explicitly describe residual data artifacts that are uniquely bound to devices, models, or their operational noise sources. In imaging, traditional sensor pattern noise is decomposed into pixel non-uniformity (manufacturing imperfections), dark current (thermal electron generation), and contributions from optics and in-camera digital processing (Matthews et al., 2018). Extraction typically employs denoising filters—high-pass or wavelet-based—to isolate high-frequency components devoid of scene content, which are then averaged across sample images to yield a device-specific or model-specific fingerprint.

A NoisePrint may also arise from algorithmic initialization (as in diffusion model generation), calibration-induced platform variations (in quantum computing), or stochastic data uncertainty (authentication biometrics, audio, synthetic imagery). These imprints persist or evolve in predictable ways, allowing cross-domain applications.

2. Deep Learning Frameworks for NoisePrint Extraction

Recent advances have centered on convolutional neural networks (CNNs) and deep generative models to extract robust NoisePrints. Notably, Cozzolino et al. propose a Siamese CNN trained with pairs of image patches, labelled by camera model and patch position, to suppress high-level content and enhance model-specific artifacts—outputting a "noiseprint" (residual map) (Cozzolino et al., 2018).

The training protocol employs pairwise Euclidean distances, softmax normalization, and a batch-wise loss designed to minimize distances for positive pairs and maximize them for negatives. Frequency-domain regularization is used to maximize the diversity of spectral components in extracted artifacts:

• For input patches $p_i$ and $p_j$: $d_{ij} = \| r_i - r_j \|^2$ (noiseprint residuals).
• Probability: $$p_i(j) = \frac{\exp(-d_{ij})}{\sum_{k \neq i}\exp(-d_{ik})}$$
• Batch loss: $$\mathcal{L}_0 = \sum_i -\log \left( \sum_{j: label=+1} p_i(j) \right )$$

For fingerprint authentication, Bayesian extensions (branching into data uncertainty maps) enable per-pixel variance estimation, using losses such as:

$$\mathcal{L} = (1/n) \sum_i \left[\frac{1}{2\sigma(x_i)^2}\|y_i-f(x_i)\|^2 + \frac{1}{2}\log \sigma(x_i)^2\right]$$

(Joshi et al., 2021).

For audio, a denoising UNet conditions on highly distorted spectrograms (CQT) and is guided by Deep Radar Feature Loss (DRFL) and Focal Tversky Loss (FTL), specifically encouraging alignment of salient fingerprint peaks used in peak-based AFP (Akesbi, 2022).

3. Forensics, Authorship, and Robust Identification

NoisePrints underpin a variety of forensic and validation tasks:

• Camera Device and Model Identification: By comparing the extracted noiseprint residual with reference patterns (via normalized cross-correlation for PRNU or mean squared error for NoisePrints), both closed-set (multi-device classification) and open-set (device verification) scenarios are supported (Cozzolino et al., 2020).
• Forgery Localization: NoisePrint-based approaches demonstrate superior sensitivity to local manipulations (splicing, inpainting, geometric distortion) compared to PRNU, due to higher signal-to-noise ratio and lower sensitivity to scene content (Cozzolino et al., 2018).
• Quantum Device Fingerprinting: Machine learning (SVMs) on time-ordered quantum measurement outcome distributions reveals device- and time-specific noiseprints, with >99% classification accuracy across IBM Quantum platforms (Martina et al., 2021).
• Fingerprint Preprocessing: Data uncertainty-guided networks produce noise variance maps that isolate regions of poor signal quality, guiding segmentation/enhancement and providing interpretable confidence in authentication (Joshi et al., 2021).
• Audio Fingerprinting: Preprocessing noisy CQTs with a DL denoising model boosts robustness of spectral peak extraction, with increases in matching precision by 1.5–2 fold under severe distortion (Akesbi, 2022).

4. Unified and Hybrid Approaches

Combining device-level (PRNU) and model-level (NoisePrint) fingerprints yields substantial improvements, particularly under practical constraints: limited images, small crops, or lossy compressed data. Fusion strategies (SVMs, Likelihood Ratio Tests, Fisher Linear Discriminant) aggregate distances from both cues for robust device identification (Cozzolino et al., 2020). This approach mitigates sensitivity losses in PRNU extraction and leverages the stability of NoisePrints, especially in high variance or partial data scenarios.

For generative models, Laplace-modeled noise-based imprints (difference vectors in latent space) are synthesized for data augmentation, generalizing the detector to unknown or future model architectures (Li et al., 12 Mar 2025). Hybrid pipelines combine NoisePrint features with high-frequency and semantic cues for state-of-the-art AI-generated image detection across GenImage, Synthbuster, and Chameleon benchmarks.

5. Cryptographically Secured Watermarking in Generative Models

The "NoisePrints" scheme formalized by (Goren et al., 15 Oct 2025) introduces a distortion-free watermarking strategy for private diffusion models. The method leverages the strong correlation between the seed-derived initial noise (via PRNG and hash function) and the image's latent representation (as encoded by a public VAE):

• Generation: $x = \mathrm{Dec}(\varepsilon(h(s)), M)$, where $h(s)$ is a cryptographic hash of seed $s$, $M$ is the model.
• Verification: Cosine similarity between $E(x)$ (VAE encoding) and $\varepsilon(h(s))$. Authorship claim $(x,s)$ is valid if $$\varphi(x, s)=\langle E(x), \varepsilon(h(s)) \rangle/(\|E(x)\|_2 \|\varepsilon(h(s))\|_2) \geq \tau$$.
• Robustness and Security: Analysis demonstrates that, for high dimensional latent space, accidental collision probability $\leq \exp(-(d-1)\tau^2/2)$ is negligible. Zero-knowledge proofs are deployed for dispute resolution and privacy-preserving verification, with the seed remaining secret.

Experiments across SD2, SDXL, Flux, and Wan confirm nearly 100% true positive rates and resilience to attacks: geometric transformation, blurring, compression, adversarial inversion, and pipeline regeneration—all with minimal computational overhead and without access to model weights.

6. Limitations, Challenges, and Future Directions

While NoisePrints methods offer heightened robustness and generalization, several limitations persist:

• Device vs. Model Discrimination: PRNU signals remain superior for precise device identification; NoisePrints excel at model attribution and manipulation localization but may leak scene content for small or texture-rich images (Cozzolino et al., 2018).
• Alignment and Preprocessing Sensitivity: Image geometric misalignment or divergent processing chains can confound NoisePrint-based localization (Cozzolino et al., 2018).
• Generative Model Adaptation: Simulated imprints (Laplace-mixed distributions) enhance generalization but depend on the representativeness of underlying models; efficacy against adversarially adapted generators remains an open question (Li et al., 12 Mar 2025).
• Cryptographic Protocol Overheads: Zero-knowledge proofs introduce commitment and verification steps whose scalability and integration with large-scale content delivery warrant further paper (Goren et al., 15 Oct 2025).

Future research aims to:

• Refine network architectures for better isolation of noise patterns.
• Integrate multi-modal cues and develop blind image clustering methods.
• Extend cryptographically bound watermarking and NoisePrint extraction to video, audio, and multimodal generative content.
• Address calibration, memory effects, and non-Markovian noise in both quantum and photographic systems.
• Strengthen adversarial robustness and legal evidentiary frameworks.

7. Summary Table: NoisePrints Across Domains

Domain	Extraction Method	Typical Application
Camera Forensics	Wavelet/Siamese CNN	Device/model identification, forgery
Audio Fingerprinting	DL Denoising + Deep Feature Loss	Robust peak extraction under noise
Quantum Devices	ML on measurement distributions	Hardware-certification, diagnostics
Fingerprint Biometrics	Bayesian CNN w/ uncertainty map	Noise-aware preprocessing, interpretability
Generative Models	Latent difference, Laplace model	AI-generated content detection
Diffusion Model Watermarking	Seed-based latent correlation/ZKP	Authorship proof under private models

NoisePrints have thus become foundational across diverse technical fields, bridging device physics, deep learning, cryptography, and domain-specific forensics, supporting efficient, robust, and secure provenance and integrity solutions for both natural and synthetic data.