Optimizing Product Provenance Verification using Data Valuation Methods
- URL: http://arxiv.org/abs/2502.15177v2
- Date: Sun, 16 Mar 2025 06:20:56 GMT
- Title: Optimizing Product Provenance Verification using Data Valuation Methods
- Authors: Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu, Brian Mayer, Victor Deklerck, Jakub Truszkowski, John C. Simeone, Jade Saunders, Chang-Tien Lu, Ruoxi Jia, Naren Ramakrishnan,
- Abstract summary: We introduce a novel data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in Stable Isotope Ratio Analysis (SIRA)<n>We validate our methodology with extensive experiments, demonstrating its potential to significantly enhance provenance verification, mitigate fraudulent trade practices, and strengthen regulatory enforcement of global supply chains.
- Score: 24.59951827145763
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or agriculture grown on illegally cleared land. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. However, the effectiveness of these models is often constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. We validate our methodology with extensive experiments, demonstrating its potential to significantly enhance provenance verification, mitigate fraudulent trade practices, and strengthen regulatory enforcement of global supply chains.
Related papers
- Federated Learning with Sample-level Client Drift Mitigation [15.248811557566128]
Federated Learning suffers from severe performance degradation due to data heterogeneity among clients.
We propose FedBSS that first mitigates the heterogeneity issue in a sample-level manner.
We also achieved effective results on feature distribution and noise label dataset setting.
arXiv Detail & Related papers (2025-01-20T09:44:07Z) - Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods.
We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results.
We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
arXiv Detail & Related papers (2024-07-19T14:53:18Z) - Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models [36.05242956018461]
In this paper, we establish a bridge between identifying detrimental training samples via influence functions and outlier gradient detection.
We first validate the hypothesis of our proposed outlier gradient analysis approach on synthetic datasets.
We then demonstrate its effectiveness in detecting mislabeled samples in vision models and selecting data samples for improving performance of natural language processing transformer models.
arXiv Detail & Related papers (2024-05-06T21:34:46Z) - InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling [66.3072381478251]
Reward hacking, also termed reward overoptimization, remains a critical challenge.
We propose a framework for reward modeling, namely InfoRM, by introducing a variational information bottleneck objective.
We show that InfoRM's overoptimization detection mechanism is not only effective but also robust across a broad range of datasets.
arXiv Detail & Related papers (2024-02-14T17:49:07Z) - Federated Learning with Projected Trajectory Regularization [65.6266768678291]
Federated learning enables joint training of machine learning models from distributed clients without sharing their local data.
One key challenge in federated learning is to handle non-identically distributed data across the clients.
We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
arXiv Detail & Related papers (2023-12-22T02:12:08Z) - A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams [0.0]
This paper presents an optimal strategy for streaming contexts with limited labeled data, introducing an adaptive technique for unsupervised regression.
The proposed method leverages a sparse set of initial labels and introduces an innovative drift detection mechanism.
To enhance adaptability, we integrate the ADWIN (ADaptive WINdowing) algorithm with error generalization based on Root Mean Square Error (RMSE)
arXiv Detail & Related papers (2023-12-12T19:23:54Z) - One-Shot Federated Learning with Classifier-Guided Diffusion Models [44.604485649167216]
One-shot federated learning (OSFL) has gained attention in recent years due to its low communication cost.
In this paper, we explore the novel opportunities that diffusion models bring to OSFL and propose FedCADO.
FedCADO generates data that complies with clients' distributions and subsequently training the aggregated model on the server.
arXiv Detail & Related papers (2023-11-15T11:11:25Z) - TRIAGE: Characterizing and auditing training data for improved
regression [80.11415390605215]
We introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors.
TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score.
We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings.
arXiv Detail & Related papers (2023-10-29T10:31:59Z) - Consistency Regularization for Generalizable Source-free Domain
Adaptation [62.654883736925456]
Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset.
Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets.
We propose a consistency regularization framework to develop a more generalizable SFDA method.
arXiv Detail & Related papers (2023-08-03T07:45:53Z) - Fine-tuning Global Model via Data-Free Knowledge Distillation for
Non-IID Federated Learning [86.59588262014456]
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint.
We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG)
Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
arXiv Detail & Related papers (2022-03-17T11:18:17Z) - Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions.
In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data.
We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
arXiv Detail & Related papers (2022-01-11T23:01:12Z) - Negative Data Augmentation [127.28042046152954]
We show that negative data augmentation samples provide information on the support of the data distribution.
We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator.
Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities.
arXiv Detail & Related papers (2021-02-09T20:28:35Z)
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.