扩散模型在指纹生成与指纹保护中的应用进展

We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models (LDM). OmniMark can encode user-specific fingerprints across diverse dimensions of the weights of the LDM, including kernels, filters, channels, and spatial domains. The LDM is fine-tuned to encode the invisible fingerprint into generated images, which can be decoded by a decoder. By altering fingerprints and re-encoding the weights, OmniMark supports efficient and scalable ad-hoc generation (

Diffusion models, especially denoising diffusion probabilistic models (DDPMs), are prevalent tools in generative AI, making their intellectual property (IP) protection increasingly important. Most existing IP protection methods for DDPMs are invasive, e.g., model watermarking, which alter model parameters and raise concerns about performance degradation, also with requirement for extra computational resources for retraining or fine-tuning. In this paper, we propose the first non-invasive fingerprinting scheme for DDPMs, requiring no parameter changes or fine-tuning, and keeping generation quality intact. We introduce a discriminative and robust fingerprint latent space based on the well-designed "crossing route" of noisy samples that span the performance border-zone of DDPMs, with only black-box access required for the diffusion denoiser in ownership verification. Extensive experiments demonstrate that our fingerprinting approach enjoys both robustness against the often-seen attacks and distinctiveness on various DDPMs, providing an alternative for protecting DDPMs’ IP rights without compromising their performance or integrity1.

The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual de-scriptions, has concurrently escalated critical societal con-cerns such as misinformation. Although providing some mitigation, traditional fingerprinting mechanisms fall short in attributing responsibility for the malicious use of syn-thetic images. This paper introduces a novel approach to model fingerprinting that assigns responsibility for the gen-erated images, thereby serving as a potential countermea-sure to model misuse. Our method modifies generative mod-els based on each user's unique digital fingerprint, imprinting a unique identifier onto the resultant content that can be traced back to the user. This approach, incorporating fine-tuning into Text-to-Image (T2I) tasks using the Stable Diffusion Model, demonstrates near-perfect attribution ac-curacy with a minimal impact on output quality. Through extensive evaluation, we show that our method outperforms baseline methods with an average improvement of 11 % in handling image post-processes. Our method presents a promising and novel avenue for accountable model distribution and responsible use. Our code is available in https://github.com/kylemin/WQUAF.

… on generating fingerprint patches and transforming fingerprints using diffusion models, a … Our approach leverages the iterative denoising process of diffusion models to refine noise …

This paper reviews recent progress in fingerprint image generation using deep generative models, with a focus on Generative Adversarial Network (GAN)-based and diffusion-based approaches. Fingerprint data are essential for biometric recognition, but collecting large and diverse datasets is difficult, especially for latent fingerprints. Early methods based on physical or statistical modeling could not produce realistic textures or sufficient diversity. With the development of deep learning, GAN-based models such as FingerGAN, PrintsGAN, and lightweight GANs have significantly improved the realism of generated fingerprints by learning data distributions directly. These methods introduce techniques such as structural constraints, multi-stage generation, and improved loss functions to enhance image quality and stability. However, GAN-based models still suffer from problems such as training instability, mode collapse, and limited control over identity consistency. To address these issues, diffusion models have recently been introduced into fingerprint generation. By gradually denoising random noise, diffusion models can generate high-quality and diverse fingerprint images with better stability. Advanced diffusion frameworks further enable controllable generation, allowing users to adjust fingerprint attributes such as style, quality, and sensor type while preserving identity information. Overall, diffusion-based methods show strong potential to become the next generation of fingerprint synthesis techniques.

… In the aforementioned diffusion model, a pivotal aspect lies in training the noise prediction … the success of the Diffusion Probabilistic Model for latent fingerprint synthesis, with results …

Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at https://github.com/YuxinWenRick/tree-ring-watermark .

In the rapidly advancing field of deep learning, a significant yet often overlooked challenge is the protection of intellectual property (IP) for models based on electroencephalography (EEG). These models, which handle sensitive and private physiological information, have not received as much attention for IP protection as their counterparts in more mainstream areas like computer vision (CV) and natural language processing (NLP). This paper introduces an innovative fingerprinting method for the first time, targeting IP protection of EEG-based models, a domain where conventional watermarking techniques fall short. We design a novel conditional diffusion model, tailored to a universal EEG format, which is the first application of diffusion models in model IP protection. Furthermore, our retrieval strategy, characterized by three distinct conditions, facilitates the construction of the fingerprint validation set from synthesized EEG samples. Experiments demonstrate that our method not only outperforms existing state-of-the-art (SOTA) protection techniques in robustness against various IP attacks but also excels in generating high-quality and high-diversity EEG samples.

The advancement of fingerprint research within public academic circles has been trailing behind facial recognition, primarily due to the scarcity of extensive publicly available datasets, despite fingerprints being widely used across various domains. Recent progress has seen the application of deep learning techniques to synthesize fingerprints, predominantly focusing on large-area fingerprints within existing datasets. However, with the emergence of AIoT and edge devices, the importance of tiny partial fingerprints has been underscored for their faster and more cost-effective properties. Yet, there remains a lack of publicly accessible datasets for such fingerprints. To address this issue, we introduce publicly available datasets tailored for tiny partial fingerprints. Using advanced generative deep learning, we pioneer diffusion methods for fingerprint synthesis. By combining random sampling with inpainting diffusion guided by feature keypoints masks, we enhance data augmentation while preserving key features, achieving up to 99.1% recognition matching rate. To demonstrate the usefulness of our fingerprint images generated using our approach, we conducted experiments involving model training for various tasks, including denoising, deblurring, and deep forgery detection. The results showed that models trained with our generated datasets outperformed those trained without our datasets or with other synthetic datasets. This indicates that our approach not only produces diverse fingerprints but also improves the model’s generalization capabilities. Furthermore, our approach ensures confidentiality without compromise by partially transforming randomly sampled synthetic fingerprints, which reduces the likelihood of real fingerprints being leaked. The total number of generated fingerprints published in this article amounts to 818,077. Moving forward, we are ongoing updates and releases to contribute to the advancement of the tiny partial fingerprint field. The code and our generated tiny partial fingerprint dataset can be accessed at https://github.com/Hsu0623/Inpainting-Diffusion-Synthetic-and-Data-Augment-with-Feature-Keypoints-for-Tiny-Partial-Fingerprints.git

… impose stringent requirements, such as generative adversarial networks (GAN) needing … of generative diffusion models (GDM), we propose a conditional generative diffusion model (…

Precise robot localization at the tree level is essential for smart agriculture applications such as precision disease management and targeted nutrient distribution. Existing methods fail to achieve the required accuracy. We propose OrchLoc, a fingerprinting-based localization solution that achieves tree-level precision using a single Long Range (LoRa) gateway. Our approach utilizes channel state information (CSI) across eight channels as a localization fingerprint. To minimize labor-intensive site surveys for fingerprint database construction and maintenance, we develop a CSI generative model (CGM) that learns the relationship between CSI vectors and their corresponding locations. The CGM is fine-tuned using CSI data from static agricultural LoRa sensor nodes, enabling continuous fingerprint database updates. Extensive experiments in two orchards demonstrate that OrchLoc effectively achieves accurate tree-level localization with minimal overhead, improving robot navigation.

In orchards, tree-level localization of robots is critical for smart agriculture applications like precision disease management and targeted nutrient dispensing. However, prior solutions cannot provide adequate accuracy. We develop our system, a fingerprinting-based localization system that can provide tree-level accuracy with only one LoRa gateway. We extract channel state information (CSI) measured over eight channels as the fingerprint. To avoid labor-intensive site surveys for building and updating the fingerprint database, we design a CSI Generative Model (CGM) that learns the relationship between CSIs and their corresponding locations. The CGM is fine-tuned using CSIs from static LoRa sensor nodes to build and update the fingerprint database. Extensive experiments in two orchards validate our system's effectiveness in achieving tree-level localization with minimal overhead and enhancing robot navigation accuracy.

… For bona fide inputs, the Frobenius norm remains consistently higher, indicating that diffusion steps preserve fingerprint relevant directions while amplifying ridge flow and minutiae. In …

… , before the improved fingerprint image is passed … diffusion and three types of non-linear oriented diffusion: coherence-enhancing, incoherenceenhancing and edge-enhancing diffusion. …

… fingerprint. We simplify the anisotropic nonlinear-diffusion in order to satisfy a real-time fingerprint … According to the local character of the fingerprint, the diffusion filter is steered by the …

Fingerprints have been crucial evidence for law enforcement agencies for a long time. Though the rapidly developing deep learning has dramatically improved the performance of the latent fingerprint recognition algorithm, a fully automated latent fingerprint identification system is still far from meeting actual needs. One major issue is the lack of publicly available latent fingerprint databases. Recently, diffusion probabilistic models have emerged as state-of-the-art deep generative methods for image synthesis. These models have better distribution coverage and less mode collapse than the popular Generative Adversarial Networks. In this paper, we propose an end-to-end latent fingerprint synthetic approach based on the improved denoising diffusion probabilistic model. The proposed approach could simultaneously generate latent, rolled, and plain fingerprints of high visual realism. Several primary degradation factors, such as various background textures, limited area of ridge patterns, and structural noise, can be directly generated without any postprocessing, unlike existing methods. We conduct NFIQ2 and perceptual analysis in the experiments to evaluate the proposed approach. The results indicate that the quality and visual realism of the proposed synthetic fingerprints is similar to the natural ones, demonstrating the effectiveness of our approach.