This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.
Introduction
In today's digital age, the influx of user-generated content, especially videos, has been immense. With this surge comes the challenge of ensuring these videos adhere to community standards and do not propagate harmful content. This is where the power of video moderation technologies comes into play, transforming the way platforms monitor and manage content.
The Need for Video Moderation
Video content can be complex and dynamic, often including elements that are not immediately apparent through conventional moderation methods. This complexity necessitates an advanced approach to moderation that can accurately and efficiently evaluate content for a variety of concerns, including explicit material, hate speech, and violence.
How Video Moderation of Azure AI Content Safety Works
Azure AI Content Safety are designed to analyze videos in real-time, scrutinizing visual elements to identify potentially harmful content. Here’s how it works:
1. Frame-by-Frame Analysis: Technology scans each key frame of the video, looking for visual indicators of harmful content.
2. Multi-Category Filtering: Azure AI Content Safety can identify and categorize harmful content across several critical domains.
- Hate: Content that promotes discrimination, prejudice, or animosity towards individuals or groups based on race, religion, gender, or other identity defining characteristics.
- Violence: Content displaying or advocating for physical harm, threats, or violent actions against oneself or others.
- Self-Harm: Material that depicts, glorifies, or suggests acts of self-injury or suicide.
- Sexual: Explicit or suggestive content, including but not limited to, nudity and intimate media.
3. Severity Indication: Azure AI Content Safety offers a unique 'Severity' metric. This metric is designed to cater to different user needs, offering four-level granularity. By providing businesses with this flexibility, they can swiftly assess the level of threat posed by video content and develop appropriate strategies to address it. This feature empowers businesses to make informed decisions and take proactive measures to ensure the video safety and integrity of their digital environment.
Video Moderation Sample Code
In our sample code, we utilize Decord, which leverages FFmpeg and hardware acceleration codecs, available at Decord GitHub. This framework significantly enhances performance, especially in terms of speed, for both sequential reading and random seeks, compared to traditional methods.
Learn more
The implementation of video moderation technologies is a significant step forward for Azure AI Content Safety in maintaining the safety and integrity of digital platforms. While not a panacea, this feature offers a robust and scalable solution to the ever-growing challenge of moderating video content. As technology evolves, so too will our ability to create safer online communities for everyone.
Azure AI Content Safety is a powerful tool which enables content flagging for industries such as Media & Entertainment, and others that require Safety & Security and Digital Content Management. We eagerly anticipate seeing your innovative implementations!
- ACOM page: https://aka.ms/acs-acom
- Documentation: https://aka.ms/acs-doc
- Studio: https://aka.ms/acsstudio
- API reference: https://aka.ms/acs-api