NDLI: The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue

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Foreword to the Applied Data Science: Invited Talks Track at KDD-2017

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

Learning Certifiably Optimal Rule Lists

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications

The Future of Data Integration

More than the Sum of its Parts: Building Domino Data Lab

The Future of Artificially Intelligent Assistants

Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks

Tripoles: A New Class of Relationships in Time Series Data

Luck is Hard to Beat: The Difficulty of Sports Prediction

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Three Principles of Data Science: Predictability, Stability and Computability

Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy

Unsupervised Network Discovery for Brain Imaging Data

Post Processing Recommender Systems for Diversity

Planning Bike Lanes based on Sharing-Bikes' Trajectories

Extremely Fast Decision Tree Mining for Evolving Data Streams

Industrial Machine Learning

Patient Subtyping via Time-Aware LSTM Networks

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform

Real-Time Optimization of Web Publisher RTB Revenues

Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management

Robust Top-k Multiclass SVM for Visual Category Recognition

Bolt: Accelerated Data Mining with Fast Vector Compression

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Customer Lifetime Value Prediction Using Embeddings

It Takes More than Math and Engineering to Hit the Bullseye with Data

KATE: K-Competitive Autoencoder for Text

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

A Data Science Approach to Understanding Residential Water Contamination in Flint

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

Planning and Learning under Uncertainty: Theory and Practice

A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Estimation of Recent Ancestral Origins of Individuals on a Large Scale

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Big Data in Climate: Opportunities and Challenges for Machine Learning

HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments

Addressing Challenges with Big Data for Media Measurement

Fast Enumeration of Large k-Plexes

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A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem

Machine Learning Software in Practice: Quo Vadis?

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

Unsupervised Feature Selection in Signed Social Networks

FIRST: Fast Interactive Attributed Subgraph Matching

Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

Designing AI at Scale to Power Everyday Life

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GRAM: Graph-based Attention Model for Healthcare Representation Learning

Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

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Algorithmic Decision Making and the Cost of Fairness

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Large Scale Sentiment Learning with Limited Labels

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

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GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources

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The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue

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Anomaly Detection with Robust Deep Autoencoders

LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity

Visualizing Attributed Graphs via Terrain Metaphor

Achieving Non-Discrimination in Data Release

The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue

Content Provider	ACM Digital Library
Author	Kanchan, Vishal Sharma, Ashlesh Subramanian, Lakshminarayanan Srinivasan, Vidyuth
Abstract	Counterfeiting of physical goods is a global problem amounting to nearly 7% of world trade. While there have been a variety of overt technologies like holograms and specialized barcodes and covert technologies like taggants and PUFs, these solutions have had a limited impact on the counterfeit market due to a variety of factors - clonability, cost or adoption barriers. In this paper, we introduce a new mechanism that uses machine learning algorithms on microscopic images of physical objects to distinguish between genuine and counterfeit versions of the same product. The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products (corresponding to the same larger product line), exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions. A key building block for our system is a wide-angle microscopy device compatible with a mobile device that enables a user to easily capture the microscopic image of a large area of a physical object. Based on the captured microscopic images, we show that using machine learning algorithms (ConvNets and bag of words), one can generate a highly accurate classification engine for separating the genuine versions of a product from the counterfeit ones; this property also holds for "super-fake" counterfeits observed in the marketplace that are not easily discernible from the human eye. We describe the design of an end-to-end physical authentication system leveraging mobile devices, portable hardware and a cloud-based object verification ecosystem. We evaluate our system using a large dataset of 3 million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. The classification accuracy is more than 98% and we show how our system works with a cellphone to verify the authenticity of everyday objects.
Starting Page	2011
Ending Page	2019
Page Count	9
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098186
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Computer vision Conventional neural networks Microscopy Physical authentication
Content Type	Text
Resource Type	Article

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