As of 2020, annual sales losses from counterfeiting in the clothing sector amounted to **** billion euros. This figure was *** billion euros for cosmetics and personal care products.

This statistic shows the value share of counterfeit and pirated goods seized worldwide in 2016, broken down by industry. In 2016, footwear products accounted for 22 percent of all fake goods seized in the world.

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Anti-Counterfeit Packaging Market Size 2025-2029

The anti-counterfeit packaging market size is forecast to increase by USD 185.9 billion, at a CAGR of 16.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the burgeoning e-commerce industry and the increasing adoption of smart and intelligent packaging solutions. As online sales continue to surge, the need for robust anti-counterfeit measures to protect brands and consumer trust becomes increasingly crucial. The advent of advanced packaging technologies, such as RFID tags, QR codes, and holograms, is transforming the industry landscape. However, the high cost of implementing these technologies poses a significant challenge for market participants. Companies must carefully weigh the benefits of enhanced security against the financial investment required. To capitalize on market opportunities, businesses should focus on developing cost-effective, scalable solutions that cater to the evolving needs of e-commerce platforms and consumers. Navigating this complex market requires a deep understanding of consumer behavior, technological advancements, and regulatory requirements. Strategic partnerships, continuous innovation, and a customer-centric approach will be key differentiators for market success.

What will be the Size of the Anti-Counterfeit Packaging Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the dynamic nature of global trade and the persistent threat of counterfeit products across various sectors. Anti-counterfeiting technologies, such as laser engraving, security threads, holographic films, and artificial intelligence (AI), are increasingly being integrated into packaging designs to ensure product traceability and maintain data integrity. Pattern recognition, image analysis, and forensic analysis play crucial roles in counterfeit detection, while fraud prevention measures, such as product lifecycle management and compliance standards, help safeguard brand reputation and consumer trust. Digital forensics and near-field communication (NFC) technologies enable real-time authenticity verification and streamline supply chain security. UV coatings and digital watermarking offer additional layers of protection, while predictive modeling and machine learning algorithms help anticipate potential threats and optimize cost reduction strategies. Industry regulations, material science, and packaging design continue to shape the landscape, with a focus on environmental impact and sustainability. Brand protection and data security remain top priorities, as e-commerce security and consumer education become increasingly essential in the digital age. RFID tags, security inks, and network security measures help maintain product authenticity throughout the supply chain, ensuring consumer confidence and adherence to industry standards. In the ever-evolving market, the integration of advanced technologies and continuous innovation is key to staying ahead of counterfeiters and maintaining the integrity of global trade.

How is this Anti-Counterfeit Packaging Industry segmented?

The anti-counterfeit packaging industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ApplicationHealthcare productsConsumer goodsOthersTechnologyAuthenticationTraceabilityGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Application Insights

The healthcare products segment is estimated to witness significant growth during the forecast period.The counterfeit packaging market is a significant concern for various industries, particularly in healthcare, as it poses potential health risks and damages brand reputation. To combat this issue, companies are developing advanced packaging solutions for product authentication, identification, and traceability. These solutions enable consumers to verify the authenticity of products and access crucial product information. For example, in March 2024, Avery Dennison Corp. Introduced a new range of smart labels incorporating Near Field Communication (NFC) technology. Consumers can use their smartphones to authenticate these products and access real-time information through dynamic QR codes. Holographic films, security threads, laser engraving, and tamper-evident labels are other anti-counterfeiting technologies that enhance product security. Artificial intelligence (AI), machine learning, and predictive modeling are also being integrated into packaging design for improved counterfeit detection and image analysis. Furthermore, data encryption, data integrity, and n

In 2024, around **** of shoppers in the Unites States looking for counterfeits found them on social media. Roughly ** percent used AI recommendations.

In 2023, around ** percent of people in the European Union tended to agree that buying counterfeit goods supported unethical behavior. Almost ** percent were in total agreement that buying fakes supported crimional organizations.

Overview

This synthetic dataset was specifically designed to support machine learning research and development in counterfeit product detection and anti-fraud systems. The dataset mimics real-world patterns found in e-commerce platforms while containing no actual sensitive or proprietary information, making it ideal for educational purposes, algorithm development, and public research.

Key Features and Data Points

Product-Level Features

Basic Product Information:

• Product ID, category, brand name, and pricing
• Six main categories: Electronics, Fashion, Cosmetics, Pharmaceuticals, Luxury Goods, and Automotive Parts
• Realistic brand variations including subtle misspellings common in counterfeit products

Seller Characteristics:

• Seller ratings (1.0-5.0 scale) with counterfeits typically showing lower ratings
• Review counts ranging from 0 to 10,000, with legitimate sellers having more reviews
• Geographic information including seller country and shipping origin

Quality Indicators:

• Number of product images (counterfeits typically have fewer)
• Product description length (counterfeits often have shorter, less detailed descriptions)
• Spelling errors count in product listings
• Certification badges and warranty information
• Domain age of seller websites

Operational Metrics:

• Shipping timeframes (counterfeits often have longer delivery times)
• Payment method variety (legitimate sellers offer more options)
• Return policy clarity and contact information completeness
• Product views, purchases, and wishlist additions

Transaction-Level Features

Transaction Details:

• Unique transaction and customer identifiers
• Transaction dates spanning one year of activity
• Customer demographics and purchase history
• Quantity and pricing information with realistic market ranges

Payment and Shipping:

• Payment methods including credit cards, PayPal, cryptocurrency, and wire transfers
• Shipping speeds and costs
• Discount patterns and promotional activity

Risk Indicators:

• Transaction velocity flags for unusual purchasing patterns
• Geolocation mismatches between customer and payment information
• Device fingerprint analysis for new vs. returning customers
• Bulk order patterns and refund request frequencies

Key Applications

• Training classification models for counterfeit product detection
• Developing fraud detection algorithms for e-commerce platforms
• Academic research in consumer protection and marketplace security
• Building risk assessment systems for online marketplaces
• Educational projects in data science and machine learning

Bakers Counterfeit Products Joint Stock Company Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This statistic shows the value of fake good imports worldwide in 2013 and 2016. In 2016, the global value of imported counterfeit goods was around 509 billion U.S. dollars.

The Anti-counterfeiting Sticker market has emerged as a crucial component in the global fight against product fraud and counterfeit goods, safeguarding brand integrity and consumer trust. As industries ranging from pharmaceuticals to luxury goods increasingly face threats from counterfeit products, anti-counterfeiti

The Anti-Counterfeiting Technologies market has become increasingly vital as global trade expands and the proliferation of counterfeit goods undermines brand integrity and consumer trust. Anti-counterfeiting technologies encompass a range of solutions designed to deter imitation and protect products across various i

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Product counterfeiting is the fraudulent reproduction of trademark, copyright, or other intellectual property related to tangible products without the authorization of the producer and motivated by the desire for profit. This study create a Product Counterfeiting Database (PCD) by assessing multiple units of analysis associated with counterfeiting crimes from 2000-2015: (1) scheme; (2) offender (individual); (3) offender (business); (4) victim (consumer); and (5) victim (trademark owner). Unique identification numbers link records for each unit of analysis in a relational database. The collection contains 5 Stata files and 1 Excel spreadsheet file. Scheme-Data.dta (n=196, 35 variables) Offender-Individual-Data.dta (n=551, 16 variables) Offender-Business-Data.dta (n=310, 5 variables) Victim-Consumer-Data.dta (n=54, 8 variables) Victim-Trademark-Owner-Data.dta (n=146, 5 variables) Relational-Data.xlsx (4 spreadsheet tabs)

The Anti-Counterfeiting Thread market is a pivotal segment of the global security and anti-counterfeiting solutions industry, designed to combat the extensive challenges posed by counterfeit goods. These specialized threads are embedded with features that authenticate products, providing brands and consumers with a

The Barcode Anti-Counterfeit Packaging Technology market is witnessing significant growth as industries across the globe strive to combat the pervasive issue of product counterfeiting. Counterfeit goods pose not only a financial threat to brands but also a potential hazard to consumer safety. By integrating barcode

The Pharmaceuticals and Food Anti-Counterfeiting Technologies market plays a critical role in safeguarding consumer health and maintaining the integrity of products in a world increasingly threatened by counterfeit goods. As the pharmaceutical and food industries continually evolve, the risk of counterfeit medicatio

The Laser Holographic Anti-Counterfeiting Label market has emerged as a vital segment within the broader anti-counterfeiting landscape, providing innovative solutions to protect brands and consumers alike. As counterfeit goods proliferate across various industries-from pharmaceuticals to luxury products-the need for

The Holographic Anti-counterfeiting Technology market has emerged as a critical segment within the broader security solutions landscape, driven by the alarming rise in counterfeit products across various industries, including pharmaceuticals, consumer goods, and luxury goods. This innovative technology utilizes intr

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

In 2023, around ***percent of consumers in the European Union aged 15 to 24 years old had intentionally purchased counterfeit goods. The trend is quite clear showing that younger shoppers were more likely to buy fakes as opposed to older buyers.

The Anti-counterfeiting Traceability System market has emerged as a critical component in today’s global economy, addressing the ever-growing menace of counterfeit goods that threaten brand integrity, consumer safety, and revenue generation across various industries. As industries such as pharmaceuticals, food and

Context

Copy Detection Patterns (CDP) are noisy, black-and-white, maximum entropy image, generated with a secret key. CDPs are designed to be sensitive to countefeit attempts, and have received significant attention from academia and industry as a practical means to facilitate detection of counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but it is not clear as of today whether it is possible to counterfeit effectively a CDP.

We therefore created this dataset to 1) stimulate research on the security of CDPs, 2) evaluate the security level against different types of copies, and 3) develop enhanced algorithms to improve detection performance (e.g. over the commonly used bit error rate which is often used in the literature).

It was shown in prior arts that the simple duplication using a copy machine is not an effective way to copy a CDP. Therefore, the most promising solution appears to be the estimation of CDP from printed-and-scanned image either by using image processing techniques, or by doing the CDP estimation using a neural network approach.

The second question is “what is the efficient CDP detector?”. Indeed, depending on the specific processing involved,

Content

The digital binary (template) CDPs have size of 52×52 pixels, with 1 pixel per element which is defined at 600 ppi, printed with 600 dpi and scanned with 2400 dpi using printer Canon IR-ADV C5535i. Therefore, the printed and scanned CDPs have the size of 208 × 208 pixels (that corresponds to 4 pixels per element) and are grayscale images. The estimation methods used in this work are the following: 1) Binarization using Otsu thresholding (called Otsu). 2) Unsharp masking followed by binarization using Otsu thresholding (called unsharp+Otsu). 3) Binarization using fully connected neural network with 2 hidden layers (called FC2). 4) Binarization using fully connected neural network with 3 hidden layers (called FC3). 5) Binarization using fully connected neural network with 4 hidden layers (called FC4). 6) Binarization using bottleneck DNN (called BN DNN). 7) Unsharp masking followed by binarization using bottleneck DNN (called unsharp+BN DNN).

The unique_cdp dataset consists of 5000 unique CDPs printed once and then estimated using six attacks (methods 1-6 from the list). It consists of 5000 digital templates and the corresponding 5000 original prints (authentic CDPs), and 4 folders of copies of the last 1500 original CDPs (the first 3500 were used for training the counterfeiting algorithm).

The batch_cdp dataset consists of CDP printed per batch, i.e. each CDP is printed multiple times. This is representative of the application of CDPs with industrial printers such as offset, flexo and rotogravure. This dataset consists of 50 unique CDPs, and each CDP is printed-and-scanned 50 times. That gives us in total 2500 printed and scanned versions of 50 unique CDP. After that we have applied 4 estimation attacks (methods 1, 2, 6 and 7 in the list) in fusion with averaging attack. The folder “fake batch” consists of 4 sub-folders with fakes obtained using estimation methods (1), (2), (6) and (7).

Acknowledgements

This research was presented in article “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector” published in WIFS 2021. Please cite the corresponding reference while using one of these databases in your academic work. E. Khermaza, I. Tkachenko, J. Picard, “Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector”, WIFS 2021, December 2021, Montpellier, France

Inspiration

These datasets are provided for academic use only with the objective of improving our understanding on the security aspects of CDPs, and can be used to address these questions: • How can we improve the detection performance? How to most efficiently separate the fake (estimated) CDPs from the original CDPs? • Id it possible to generate CDP copies that can be undetectable ?

If you would like to test your own copies, you can try on your printer by printing them at 600ppi. You may also reach out to us so we can print and scan them in comparable conditions as used for this dataset.