Mitigation Filters & Watermarking

• Mitigation Filters and Watermarking are complementary methods that secure digital content by embedding signals and applying adaptive filters.
• The field utilizes spatial, transform, and dynamic techniques to achieve imperceptible embedding, robustness, and high payload capacity across multiple domains.
• Advanced mitigation filters enhance watermark resilience by leveraging cryptographic methods and statistical tests to detect and counter digital attacks.

Mitigation filters and watermarking constitute parallel and, at times, complementary strands in the security and authenticity assurance of digital content and control systems. Watermarking, in its various forms, embeds secret or overt information into signals to provide copyright protection, provenance, or attack detection, while mitigation filters serve as either pre- or post-processing steps to defend against or to remove such embedded traces—either to frustrate adversaries or, conversely, to test system resilience. The field encompasses a wide array of domains, from digital images and neural models to control and cyber-physical systems.

1. Historical Context, Fundamental Properties, and Architectures

Modern digital watermarking is conceptually rooted in analog watermarks found in papermaking in 13th-century Italy, but extends this idea through embedding discrete or continuous patterns (bits, signals, or codes) into digital content (Perwej et al., 2012). Key desiderata for any digital watermarking system are:

• Imperceptibility (Fidelity): Embedded marks minimally affect the signal’s appearance or function.
• Robustness: The watermark survives common transformations or attacks, such as compression, filtering, cropping, or adversarial manipulations.
• Payload Capacity: The amount of information that can be embedded.
• Security: Only holders of the key or detector can reliably assert the mark’s presence.
• Computational Efficiency: Embedding and detection impose low overhead, especially for real-time or low-resource applications (Talathi et al., 7 Jun 2025).

Digital watermarking systems span diverse architectures:

• Spatial domain methods (e.g., LSB substitution) alter pixels directly for simplicity and high fidelity.
• Transform domain approaches (DCT, DWT, FWT) leverage frequency or multi-resolution analysis for improved robustness (Perwej et al., 2012, Kadkhodaei et al., 2020, Talathi et al., 7 Jun 2025).
• Key-based cryptographic methods tie ownership or provenance claims to strong signatures or hash functions (Abdelaziz et al., 9 Feb 2025, Nagm et al., 2021).
• Function space and neural field watermarking embeds marks not in observable samples but in underlying continuous representations (Zhu et al., 2023).

Mitigation filters—whether robust embedding designed to survive attacks or methodologies for watermark removal—are engineered both to test and to advance watermarking resilience.

2. Core Methodologies and Techniques

a. Image and Video Watermarking

Spatial and Transform Domain:

• Edge detection (e.g., Gabor filters) enhances selection of perceptually insensitive regions for embedding (Perwej et al., 2012).
• LSB substitution methods replace image pixel bits with watermark bits, often controlled by a pseudo-random sequence, ensuring that only parties with the correct key can extract or verify the mark (Perwej et al., 2012, Nagm et al., 2021).
• Frequency (DCT/DWT/FWT) approaches embed marks in frequency or wavelet coefficients, especially mid-band DCT or low-frequency FWT, balancing perceptual quality and robustness against standard image attacks (Perwej et al., 2012, Kadkhodaei et al., 2020, Talathi et al., 7 Jun 2025).

Video Watermarking:

• Video watermarking extends these techniques by using per-frame or key-frame strategies and aggregates decisions using schemes like logit-mean, median, thresholding, or majority voting to deal with temporal and adversarial perturbations (Jiang et al., 27 May 2025).

b. Mitigation and Robustness Enhancement

• Adaptive strength factors distribute watermark energy according to block or region complexity, using local statistics (e.g., edge densities or coefficient variance) to boost resilience without degrading perceived quality (Kadkhodaei et al., 2020).
• Mosaic-based redundancy and multi-instance embedding further mitigate bit errors under attack (Talathi et al., 7 Jun 2025).
• Certified device architectures embed unique cryptographic identifiers into images at the source, introducing an authentication pipeline that combines hardware, cryptography, and steganography (Nagm et al., 2021).

c. Dynamic and Multiplicative Watermarking in Control Systems

• Dynamic watermarking injects private, unpredictable signals into control actuation, with statistical tests (often leveraging random matrix theory and concentration inequalities) used to monitor for inconsistencies resulting from adversarial sensor attacks (Hespanhol et al., 2019, Olfat et al., 2020).
• Covariance-robust dynamic watermarking constructs composite likelihood ratio tests robust to unknown or varying measurement noise covariances, with guarantees linked to fair hypothesis testing (Olfat et al., 2020).
• Multiplicative watermarking, designed via output-to-output $\ell_2$-gain minimization (subject to LMI constraints), provides a sufficient condition for bounded attack detectability in cyber-physical systems (Gallo et al., 2021).

d. Neural and EEG Model Watermarking

• Cryptographic wonder filters, keyed by digital signatures, are embedded during training of neural networks for IP protection. Authentication leverages collision-resistant hashing and public-key encryption; “null embedding” locks the model to the owner by forcing neutral responses to similar but incorrect triggers (Abdelaziz et al., 9 Feb 2025).

e. Multi-Key and Security Game Approaches

• Multi-key watermarking randomizes the choice of secret key per sample and verifies authenticity by requiring that only one key triggers a positive detection. Theoretical guarantees bound adversarial forging success by approximately $1/r$ for $r$ keys, independent of watermark modality (Aremu et al., 10 Jul 2025).

3. Robustness Evaluation and Mitigation Filters

a. Attack Classes and Metrics

• Robustness is evaluated using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bit Error Rate (BER), Normalized Cross Correlation (NC), and—for more specialized domains—metrics like masked SSIM, detection AUC, and output-to-output $\ell_2$-gain (Perwej et al., 2012, Kadkhodaei et al., 2020, Zhu et al., 2023, Arabi et al., 5 Dec 2024, Tallam et al., 13 May 2025).
• Standard attacks include signal-level distortions (Gaussian noise, filtering, compression), geometric transformations (cropping, scaling, rotation), and adversarial manipulations (white-box or black-box attacks targeting detection mechanisms) (Kadkhodaei et al., 2020, Jiang et al., 27 May 2025).
• Semantic and regenerative removal attacks leverage vision-LLMs, segmentation, and diffusion models to remove watermarks while preserving core content, revealing critical weaknesses in state-of-the-art watermarking (Tallam et al., 13 May 2025, Liu et al., 7 Oct 2024).

b. Mitigation and Adaptivity

• Adaptive regeneration filters, using semantic and spatial information to guide diffusion-based inpainting or reconstruction, provide controllable tradeoffs between watermark removal and preservation of image content, challenging the efficacy of robust embedding (Liu et al., 7 Oct 2024, Tallam et al., 13 May 2025).
• In control systems, sensor switching based on watermark-detected anomalies allows the system to gracefully degrade (switching to attack-resistant but lower-accuracy sensors) and then return to high accuracy in safe conditions (Hespanhol et al., 2019).

c. Two-Stage and Function Space Methods

• Two-stage watermarking frameworks (e.g., WIND) couple large, non-colliding key spaces (initial noise vectors and group-level Fourier embeddings) with efficient detection via group retrieval and candidate noise matching, achieving distortion-free embedding and resistance to both removal and forgery (Arabi et al., 5 Dec 2024).
• Function-space watermarking for meshes (as in FuncMark) embeds the mark into the signed distance field, so any mesh extracted (by any tessellation or isosurfacing method) will retain the watermark, thus mitigating attacks that generate alternate discretizations (Zhu et al., 2023).

4. Applications and Implications

• Copyright and Provenance: Watermarking serves as a decisive mechanism for copyright assertion and tracing unauthorized distribution in digital media, including images, video, and audio (Perwej et al., 2012, Nagm et al., 2021).
• Authentication and Integrity: Hybrid cryptographic-watermarking techniques offer tamper-evidence and forensic suitability, especially in contexts such as legal evidence and certified digital camera architectures (Nagm et al., 2021).
• Neural/IP Protection: Specialized frameworks leverage cryptographic signatures and lock-down mechanisms to guarantee ownership in neural networks used in medical, biometric, or proprietary applications, deterring piracy and secondary watermarking attempts (Abdelaziz et al., 9 Feb 2025).
• Cyber-Physical Systems: Dynamic and multiplicative watermarking facilitates attack detection, real-time system switching, and secure operation in autonomous vehicles, power grids, and critical infrastructure (Hespanhol et al., 2019, Olfat et al., 2020, Gallo et al., 2021).
• IoT and Real-Time Multimedia: Hybrid resource-efficient designs such as FWT-AQIM meet the twin goals of low power and high robustness required for real-time and embedded applications (Talathi et al., 7 Jun 2025).

5. Security, Limitations, and Future Directions

• Security Games: Formal security models articulate authentication criteria and adversarial forging/stealing threats—and multi-key watermarking provides measurable near-optimal defense (Aremu et al., 10 Jul 2025).
• Fairness in Detection: Composite statistical tests ensure false alarm rates in dynamic watermarking are independent of sensor or environment (“protected attributes”), aligning with requirements in algorithmic fairness (Olfat et al., 2020).
• Limitations and Trade-offs: Most robust watermarking algorithms must trade off among payload, imperceptibility, robustness, and computational constraint. Advanced attacks, especially semantics-aware regeneration or content-preserving inpainting, expose vulnerabilities in even semantic content-aware watermarks (Tallam et al., 13 May 2025).
• Continued Arms Race: As mitigation filters and attack strategies advance, future watermarking schemes must evolve similarly, potentially by embedding multi-layered, context-aware, or temporally-consistent signals (especially for video and generative neural systems) and incorporating cryptographic and hardware-level coordination.

6. Representative Formulas and Diagrams

• Gabor Filter: $$g(x, y) = \exp\left(-\frac{x'^2 + \gamma^2 y'^2}{2\sigma^2}\right) \cos\left(\frac{2\pi x'}{\lambda} + \phi\right)$$ with $x' = x \cos\theta + y \sin\theta$, etc. (Perwej et al., 2012)
• DCT and FWT: $$F(u, v) = a(u) a(v) \sum_{m=0}^{N-1} \sum_{n=0}^{N-1} f(m, n) \cos\left(\frac{(2m+1)u\pi}{2N}\right)\cos\left(\frac{(2n+1)v\pi}{2N}\right)$$
• PSNR: $$\mathrm{PSNR} = 10\ \log_{10}\left(\frac{\mathrm{MAX}^2}{\mathrm{MSE}}\right)$$ (Perwej et al., 2012, Kadkhodaei et al., 2020)
• Dynamic Watermarking Test Vector: $$\psi_n^\top = \left[(C\hat{x}_n - y_n)^\top,\ e_{n-k'}^\top\right]$$ (Hespanhol et al., 2019, Olfat et al., 2020)
• Multi-Key Watermarking Acceptance:

$$Z_i(x) = \mathbb{1}\{\mathrm{Detect}_{k_i}(x) > \tau\},\quad \sum_{i=1}^r Z_i(x) = 1\ \Longrightarrow\ \text{accept}$$

(Aremu et al., 10 Jul 2025)

---

Mitigation filters and watermarking together define a dynamic field that spans information security, signal processing, cryptography, and adversarial learning. Their interplay shapes the evolution of secure digital communications, authenticated media, resilient control, and intellectual property protection across a diverse application landscape.