DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking

• URL: http://arxiv.org/abs/2406.02836v2
• Date: Thu, 20 Jun 2024 15:25:05 GMT
• Title: DREW : Towards Robust Data Provenance by Leveraging Error-Controlled Watermarking
• Authors: Mehrdad Saberi, Vinu Sankar Sadasivan, Arman Zarei, Hessam Mahdavifar, Soheil Feizi,
• Abstract summary: We propose Data Retrieval with Error-corrected codes and Watermarking (DREW) DREW randomly clusters the reference dataset and injects unique error-controlled watermark keys into each cluster. After locating the relevant cluster, embedding vector similarity retrieval is performed within the cluster to find the most accurate matches.
• Score: 58.37644304554906
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying the origin of data is crucial for data provenance, with applications including data ownership protection, media forensics, and detecting AI-generated content. A standard approach involves embedding-based retrieval techniques that match query data with entries in a reference dataset. However, this method is not robust against benign and malicious edits. To address this, we propose Data Retrieval with Error-corrected codes and Watermarking (DREW). DREW randomly clusters the reference dataset, injects unique error-controlled watermark keys into each cluster, and uses these keys at query time to identify the appropriate cluster for a given sample. After locating the relevant cluster, embedding vector similarity retrieval is performed within the cluster to find the most accurate matches. The integration of error control codes (ECC) ensures reliable cluster assignments, enabling the method to perform retrieval on the entire dataset in case the ECC algorithm cannot detect the correct cluster with high confidence. This makes DREW maintain baseline performance, while also providing opportunities for performance improvements due to the increased likelihood of correctly matching queries to their origin when performing retrieval on a smaller subset of the dataset. Depending on the watermark technique used, DREW can provide substantial improvements in retrieval accuracy (up to 40\% for some datasets and modification types) across multiple datasets and state-of-the-art embedding models (e.g., DinoV2, CLIP), making our method a promising solution for secure and reliable source identification. The code is available at https://github.com/mehrdadsaberi/DREW

Related papers

• Hierarchical Sparse Representation Clustering for High-Dimensional Data Streams [16.228652652243888]
  We propose a hierarchical sparse representation clustering (HSRC) method for clustering high-dimensional data streams. The experimental results obtained on several benchmark datasets demonstrate the effectiveness and robustness of HSRC.
  arXiv  Detail & Related papers  (2024-09-07T03:40:55Z)
• SparseCL: Sparse Contrastive Learning for Contradiction Retrieval [87.02936971689817]
  Contradiction retrieval refers to identifying and extracting documents that explicitly disagree with or refute the content of a query. Existing methods such as similarity search and crossencoder models exhibit significant limitations. We introduce SparseCL that leverages specially trained sentence embeddings designed to preserve subtle, contradictory nuances between sentences.
  arXiv  Detail & Related papers  (2024-06-15T21:57:03Z)
• Fact Checking Beyond Training Set [64.88575826304024]
  We show that the retriever-reader suffers from performance deterioration when it is trained on labeled data from one domain and used in another domain. We propose an adversarial algorithm to make the retriever component robust against distribution shift. We then construct eight fact checking scenarios from these datasets, and compare our model to a set of strong baseline models.
  arXiv  Detail & Related papers  (2024-03-27T15:15:14Z)
• Graph-based Active Learning for Entity Cluster Repair [1.7453520331111723]
  Cluster repair methods aim to determine errors in clusters and modify them so that each cluster consists of records representing the same entity. Current cluster repair methodologies assume duplicate-free data sources, where each record from one source corresponds to a unique record from another. Recent approaches apply clustering methods in combination with link categorization methods so they can be applied to data sources with duplicates. We introduce a novel approach for cluster repair that utilizes graph metrics derived from the underlying similarity graphs.
  arXiv  Detail & Related papers  (2024-01-26T16:42:49Z)
• Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation [65.268245109828]
  Online deep clustering refers to the joint use of a feature extraction network and a clustering model to assign cluster labels to each new data point or batch as it is processed. While faster and more versatile than offline methods, online clustering can easily reach the collapsed solution where the encoder maps all inputs to the same point and all are put into a single cluster. We propose a method that does not require data augmentation, and that, differently from existing methods, regularizes the hard assignments.
  arXiv  Detail & Related papers  (2023-03-29T08:23:26Z)
• CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularization [3.3711670942444014]
  Federated learning enables edge devices to train a global model collaboratively without exposing their data. We tackle a new type of Non-IID data, called cluster-skewed non-IID, discovered in actual data sets. We propose an aggregation scheme that guarantees equality between clusters.
  arXiv  Detail & Related papers  (2023-02-21T02:53:37Z)
• Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval [25.402767809863946]
  Inverted file structure is a common technique for accelerating dense retrieval. In this work, we present the Hybrid Inverted Index (HI$2$), where the embedding clusters and salient terms work to accelerate dense retrieval.
  arXiv  Detail & Related papers  (2022-10-11T15:12:41Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies [0.0]
  In this paper, we propose an INtelligent Summarization approach for IDENTifying hidden anomalies, called INSIDENT. Our approach is a clustering-based algorithm that dynamically maps original feature space to a new feature space by locally weighting features in each cluster. Besides, selecting representatives based on cluster size keeps the same distribution as the original data in summarized data.
  arXiv  Detail & Related papers  (2020-12-24T23:22:57Z)
• Open-sourced Dataset Protection via Backdoor Watermarking [87.15630326131901]
  We propose a emphbackdoor embedding based dataset watermarking method to protect an open-sourced image-classification dataset. We use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model.
  arXiv  Detail & Related papers  (2020-10-12T16:16:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.