This is the replication package containing the dataset for code detection of AIGC Detectors, code to setup and run AIGC Detectors and the results from AIGC Detectors

IVY-FAKE: Unified Explainable Benchmark and Detector for AIGC Content

This repository provides the official implementation of IVY-FAKE and IVY-xDETECTOR, a unified explainable framework and benchmark for detecting AI-generated content (AIGC) across both images and videos.

  🔍 Overview

IVY-FAKE is the first large-scale dataset designed for multimodal explainable AIGC detection. It contains:

150K+ training samples (images + videos) 18.7K evaluation samples Fine-grained… See the full description on the dataset page: https://huggingface.co/datasets/AI-Safeguard/Ivy-Fake.

🖼️ DiffSeg20k -- A multi-turn diffusion-editing dataset for localized AIGC detection

A dataset for segmenting diffusion-based edits — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model

  📁 Dataset Usage

xxxxxxxx.image.png: Edited images. Each image may have undergone 1, 2, or 3 editing operations. xxxxxxxx.mask.png: The corresponding mask indicating edited regions, where pixel values encode both the type of… See the full description on the dataset page: https://huggingface.co/datasets/Chaos2629/Diffseg20k.

The AIGC (AI-Generated Content) cloud computing platform market is experiencing explosive growth, driven by the increasing adoption of AI technologies across various sectors. The market's expansion is fueled by the need for scalable and cost-effective infrastructure to support the computationally intensive tasks involved in generating AI-driven content such as images, videos, and text. Key drivers include the rising demand for personalized content, advancements in generative AI models, and the increasing availability of large datasets for training these models. Major cloud providers like AWS, Azure, Google Cloud, Alibaba Cloud, Huawei Cloud, and Tencent Cloud are at the forefront of this market, offering specialized services and infrastructure tailored to AIGC workloads. While the market faces restraints such as the high cost of implementation, concerns regarding data privacy, and the need for specialized expertise, the long-term growth potential remains substantial. We estimate a market size of $15 billion in 2025, growing at a Compound Annual Growth Rate (CAGR) of 35% between 2025 and 2033. This projection considers the rapid advancements in AI technology and increasing enterprise adoption. The market is segmented by deployment model (cloud, on-premises), application (image generation, video generation, text generation), and industry vertical (media & entertainment, advertising, healthcare). North America currently holds the largest market share due to early adoption and strong technological infrastructure, but Asia-Pacific is expected to witness significant growth in the coming years. The competitive landscape is marked by intense rivalry among major cloud providers, each vying for market dominance through innovative offerings, strategic partnerships, and acquisitions. The focus is shifting towards offering comprehensive platforms that encompass model training, deployment, and management capabilities, simplifying the AIGC development process for businesses. Future growth will be shaped by factors such as the development of more sophisticated AI models, improved infrastructure capabilities, and the increased integration of AIGC into existing workflows. The market will continue to attract significant investments as businesses recognize the potential of AIGC to transform various aspects of their operations. Continued innovation in model efficiency and reduced computational costs will further fuel market expansion and broader accessibility.

Check Market Research Intellect's Artificial Intelligence Generated Content (AIGC) Market Report, pegged at USD 11.3 billion in 2024 and projected to reach USD 38.2 billion by 2033, advancing with a CAGR of 15.4% (2026–2033).Explore factors such as rising applications, technological shifts, and industry leaders.

AIGC Software Support Market Outlook

The global AIGC (Artificial Intelligence Generated Content) Software Support market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach USD 6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 21.5% during the forecast period. This remarkable growth factor can be attributed to the increasing adoption of AI technologies across various industries, the rising need for enhanced customer experience, and the demand for cost-effective and efficient content generation solutions. The AIGC market is poised for substantial expansion as businesses globally recognize the value of automated content generation in achieving operational efficiency and competitive advantage.

The surge in demand for AI-powered systems is a significant growth driver for the AIGC Software Support market. As industries such as healthcare, finance, and media increasingly rely on AI for intelligent content generation, the need for robust software support has become paramount. AI-generated content can drastically improve customer engagement, streamline operations, and reduce human error, propelling the demand for AIGC solutions. Moreover, advancements in machine learning algorithms and natural language processing (NLP) are further enhancing the capabilities of AIGC software, making them more sophisticated and reliable.

Additionally, the rising emphasis on personalized customer experiences is fueling the growth of the AIGC Software Support market. Businesses are leveraging AI-generated content to tailor their communications and marketing strategies, thereby improving customer satisfaction and brand loyalty. For example, in the retail and e-commerce sectors, AI can generate personalized product descriptions, recommendations, and promotional content, significantly enhancing the shopping experience. Similarly, in the healthcare sector, AI-generated content aids in creating personalized health plans, patient communication, and medical documentation, thereby improving patient care and operational efficiency.

The cost-effectiveness and scalability of AIGC solutions are also key growth factors. Traditional content generation methods are time-consuming and resource-intensive, involving significant human effort. In contrast, AI-driven content generation can produce high-quality content at a fraction of the cost and time, making it an attractive option for businesses of all sizes. This efficiency is particularly beneficial for small and medium enterprises (SMEs) that may have limited resources but require high-quality content to compete effectively in the market.

AI Translation is becoming an integral part of the AIGC Software Support market, as businesses seek to overcome language barriers and expand their global reach. The ability to automatically translate content into multiple languages using AI-driven tools not only enhances communication but also improves customer engagement across diverse markets. This capability is particularly valuable for multinational corporations and e-commerce platforms that operate in various regions. By leveraging AI Translation, companies can offer personalized and localized content to their customers, thereby enhancing user experience and building stronger brand loyalty. Furthermore, advancements in natural language processing are making AI Translation more accurate and reliable, enabling businesses to maintain consistency and quality in their communications.

On a regional level, North America is expected to dominate the AIGC Software Support market due to the widespread adoption of AI technologies and the presence of major AI companies in the region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increasing investments in AI, and a burgeoning tech-savvy population. Europe, with its strong technological infrastructure and regulatory support for AI initiatives, is also expected to contribute significantly to market growth.

Component Analysis

The AIGC Software Support market can be segmented by component into software and services. The software segment encompasses various AI-driven platforms and tools designed for content generation and support. This segment is expected to witness substantial growth due to continuous advancements in AI algorithms and the increasing complexity of content requirements. As businesses demand more sophisticated and reliable AI solutions, software pro

The report on AIGC Large Model covers a summarized study of several factors supporting market growth, such as market size, market type, major regions, and end-user applications. The report enables customers to recognize key drivers that influence and govern the market.

Human Fighting

## Overview

Human Fighting is a dataset for object detection tasks - it contains Human Fighting annotations for 311 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

🔥 (ACL2025) AIGUARD: A Benchmark and Lightweight Detection of E-commerce AIGC Risks 🔥

The statistic of the dataset are shown in the table below.

Category Total Positive Negative Ratio

Abnormal Body 76,800 12,768 64,032 1:5

Violating Physical Laws 90,880 15,154 75,726 1:5

Misleading or Illogical Context 65,280 10,847 54,433 1:5

Harmful or Problematic Message 20,460 5,116 15,344 1:3

  🔨 Dataset Description

AIGUARD, the first comprehensive AIGC bad… See the full description on the dataset page: https://huggingface.co/datasets/yinyueguilai/AIGUARD_dataset.

The forensic examination of AIGC(Artificial Intelligence Generated Content) faces poses a contemporary challenge within the realm of color image forensics. A myriad of artificially generated faces by AIGC encompasses both global and local manipulations. While there has been noteworthy progress in the forensic scrutiny of fake faces, current research primarily focuses on the isolated detection of globally and locally manipulated fake faces, thus lacking a universally effective detection methodology. To address this limitation, we propose a sophisticated forensic model that incorporates a dual-stream framework comprising quaternion RGB and PRNU(Photo Response Non-Uniformity). The PRNU stream extracts the “camera fingerprint” feature by discerning the non-uniform response of the image sensor under varying lighting conditions, thereby encapsulating the overall distribution characteristics of globally manipulated faces. The quaternion RGB stream leverages the inherent nonlinear properties of quaternions and their informative representation capabilities to accurately describe changes in image color, background, and spatial structure, facilitating the meticulous capture of nuanced local distinctions between locally manipulated faces and real faces. Ultimately, we integrate the two streams to establish the exchange of feature information between PRNU and quaternion RGB streams. This strategic integration fully exploits the complementarity between two streams to amalgamate local and global features effectively. Experimental results obtained from diverse datasets underscore the advantages of our method in terms of accuracy, achieving a detection accuracy of 96.81%.

The AIGC Large Model market, an integral part of the artificial intelligence landscape, is experiencing extraordinary growth as organizations across various industries increasingly recognize the potential of generative AI. These large language models, capable of producing human-like text and engaging in complex dial

Dec Merge

## Overview

Dec Merge is a dataset for object detection tasks - it contains Hat Normalglasses Helmet Glasses Vest No Helmet No Glasses No Vest annotations for 1,621 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Card for Forensics-Bench

Repository: https://github.com/Forensics-Bench/Forensics-Bench Paper: https://arxiv.org/pdf/2503.15024 Point of Contact: Jin Wang

  Introduction

Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, law, and etc. To detect the ever-increasingly diverse malicious fake media in the new era of AIGC, recent studies… See the full description on the dataset page: https://huggingface.co/datasets/Forensics-bench/Forensics-bench.

The forensic examination of AIGC(Artificial Intelligence Generated Content) faces poses a contemporary challenge within the realm of color image forensics. A myriad of artificially generated faces by AIGC encompasses both global and local manipulations. While there has been noteworthy progress in the forensic scrutiny of fake faces, current research primarily focuses on the isolated detection of globally and locally manipulated fake faces, thus lacking a universally effective detection methodology. To address this limitation, we propose a sophisticated forensic model that incorporates a dual-stream framework comprising quaternion RGB and PRNU(Photo Response Non-Uniformity). The PRNU stream extracts the “camera fingerprint” feature by discerning the non-uniform response of the image sensor under varying lighting conditions, thereby encapsulating the overall distribution characteristics of globally manipulated faces. The quaternion RGB stream leverages the inherent nonlinear properties of quaternions and their informative representation capabilities to accurately describe changes in image color, background, and spatial structure, facilitating the meticulous capture of nuanced local distinctions between locally manipulated faces and real faces. Ultimately, we integrate the two streams to establish the exchange of feature information between PRNU and quaternion RGB streams. This strategic integration fully exploits the complementarity between two streams to amalgamate local and global features effectively. Experimental results obtained from diverse datasets underscore the advantages of our method in terms of accuracy, achieving a detection accuracy of 96.81%.

Koala

## Overview

Koala is a dataset for object detection tasks - it contains Koala annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

PPE Merged

## Overview

PPE Merged is a dataset for object detection tasks - it contains No Helmet No Glasses No Vest Helmet Glasses Vest annotations for 1,200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Check out Market Research Intellect's AIGC Generates Algorithmic Models And Datasets Market Report, valued at USD 2.5 billion in 2024, with a projected growth to USD 12.8 billion by 2033 at a CAGR of 22.5% (2026-2033).

Customer Support Software Systems Market Outlook

The global customer support software systems market size was valued at approximately $10 billion in 2023 and is projected to reach a staggering $25.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.8% during the forecast period. This robust growth can be attributed to several factors, including the increasing demand for enhanced customer service experiences, the proliferation of digital customer engagement channels, and the integration of artificial intelligence technologies in customer support systems. As businesses recognize the strategic importance of delivering superior customer service to drive customer loyalty and brand differentiation, the adoption of customer support software systems is expected to continue its upward trajectory.

The growth of the customer support software systems market is driven significantly by the ongoing digital transformation across various industry verticals. Organizations are increasingly adopting digital tools to streamline their customer support processes, enhance efficiency, and improve customer satisfaction levels. The integration of advanced technologies, such as artificial intelligence, machine learning, and chatbots, into customer support software is revolutionizing the way businesses interact with their customers. These technologies enable businesses to automate routine queries, provide real-time assistance, and deliver personalized customer experiences. Furthermore, the shift towards omnichannel customer engagement strategies is prompting businesses to invest in comprehensive customer support solutions that can seamlessly integrate with multiple communication channels, thereby enhancing the overall customer experience.

Another crucial factor contributing to the growth of the customer support software systems market is the increasing focus on customer experience management. Businesses are realizing that providing excellent customer support is not just a cost center but a strategic differentiator that can drive customer retention and increase revenue. As a result, there is a growing emphasis on investing in robust customer support solutions that can help businesses understand customer needs, preferences, and pain points more effectively. Additionally, the rising trend of remote work and the need for flexible, cloud-based solutions have further accelerated the adoption of customer support software systems, as they enable organizations to manage customer interactions efficiently regardless of the geographical location of their support teams.

Furthermore, the market is witnessing significant growth due to the increasing demand for data-driven insights and analytics. Customer support software systems are being increasingly equipped with advanced analytics capabilities that allow businesses to gain actionable insights from customer interactions and feedback. By analyzing customer data, organizations can identify trends, improve service quality, and make informed decisions to enhance customer satisfaction. This data-driven approach is becoming a critical component of customer support strategies, as it enables businesses to proactively address customer issues, optimize support processes, and ultimately drive business growth. As the emphasis on customer data analysis continues to grow, the demand for sophisticated customer support software systems is expected to rise accordingly.

Component Analysis

The customer support software systems market is segmented into software and services when analyzed by component. Software solutions form the backbone of the customer support software systems market, offering a wide range of functionalities to manage customer interactions efficiently. These software solutions include ticketing systems, live chat applications, knowledge management, and customer relationship management (CRM) platforms. As businesses strive to offer seamless customer experiences, the demand for integrated software solutions that can unify customer data across various touchpoints has been on the rise. Additionally, software vendors are continuously enhancing their offerings with new features and capabilities, such as AI-powered chatbots and predictive analytics, to cater to the evolving needs of businesses.

Aigc Software Support is becoming increasingly crucial in the customer support software systems market as businesses strive to enhance their service offerings with cutting-edge technology. This support involves the integration of artificial intelligence-generated content (AIG