The digital landscape is fraught with the perpetual challenge of combating harmful content. Amid this relentless pursuit, a standout company armed with advanced video identification technology has emerged. Videntifier Technologies has risen as a formidable force in the relentless battle against the proliferation of malicious content. As a result of utilizing advanced technology, Videntifier has forged strategic partnerships with renowned organizations such as Interpol, National Center for Missing & Exploited Children (NCMEC), and major technology giants. Their shared mission — to combat the proliferation of illegal content that plagues the online world.
With the exponential growth of video-sharing platforms as vital mediums of communication and self-expression, it has become increasingly challenging to ensure the safety and security of internet users. Recognizing this pressing issue, the company has emerged as a pioneer in video identification by addressing the fundamental flaw inherent in hash technologies. The Videntifier team has revolutionized video identification by transforming local descriptor hashes into a powerful, flexible solution.
Read on to explore the technological capabilities of Videntifier and examine how their innovative approach is revolutionizing the video identification field.
Hash technologies are methods of file fingerprinting used to distinguish one image or video from another They are often leveraged by online platforms, law enforcement and abuse hotlines to help detect modified illicit content reposted to the web. However, until recently, most hash types have proved themselves inadequate in providing a complete solution. For example, when identifying modified videos, the commonly employed cryptographic and perceptual hashes face inherent limitations. Cryptographic hashes can only identify exact matches, meaning that this method completely falls apart when even the slightest alteration is made to a file. Meanwhile, perceptual hashes are better suited to detecting minor modifications but less effective at detecting larger changes, such as cropping or adding pictures within pictures.
While these widely used identification methods hold value, they fail to comprehensively address the issue, often allowing reposted harmful content to remain undetected and persist online for extended periods. As a result, individuals continue to be re-victimized. This highlights the pressing need for more robust video identification solutions.
Videntifier adopts an innovative approach by leveraging local descriptor hashes, which possess unique capabilities not found in other hash types. Using this specialized hash type, videos can be identified even after they have been altered with such modifications as cropping, bordering, embedding, or picture-in-picture. Equipped with the necessary sophistication and power, local descriptors are invaluable tools for platforms that foster a safer online environment, particularly in accurately detecting known CSAM.
In the past, implementing local descriptor hashes has faced scalability and computational efficiency challenges, reducing their effectiveness in video identification. Yet, Videntifier’s team of skilled engineers has addressed these issues head-on, proposing a two-pronged approach. Combining their in-depth visual fingerprinting algorithm and NV-Tree’s patent-protected database index, they can provide quick and efficient video identification tools to trust and safety teams.
With Videntifier’s descriptor extraction component, video frames are processed, and visual descriptors are extracted as images. Known for its resource efficiency, this methodology involves fine-tuning the extraction process to deliver the optimal selection of frames, descriptors and levels of detail necessary to identify the target successfully. Videntifier’s NV-Tree database structure, a composite of multiple hotline hash databases (most notably NCMEC’s), is capable of accommodating billions of descriptors that facilitate rapid search across millions of hours of video and billions of images. The inherent scalability of this technique ensures the practicality and effectiveness of local descriptor searches.
The NV-Tree’s unique structure supports collaboration among online platforms and law enforcement agencies, with the added benefit of protecting client and user data by utilizing one-way encoding techniques for queries and preserving user privacy throughout the process. Through the exchange of hashes representing identified instances of CSAM, these entities can forge a united front in their ongoing battle against harmful content. Their collective efforts effectively thwart the dissemination of such materials and expedite their removal from their respective platforms. This collaborative approach not only increases the potency of video identification operations, it also provides a more effective and responsive countermeasure against the illicit circulation of such material.
Trust and safety teams benefit from Videntifier’s cutting-edge video identification solution. Within seconds, moderators can easily query and identify content utilizing its rapid and precise search capabilities. Designed to facilitate a safer and more secure online environment, Videntifier’s solution offers a variety of search query options and advanced exposure reduction measures for moderators.
With an eye toward the future, the company is committed to preventing the proliferation of CSAM and other malicious content online. A key component of their mission involves supporting the dedicated moderators and analysts monitoring such distressing material. Through its state-of-the-art technology, Videntifier aims to alleviate the burden on these professionals and mitigate the psychological toll associated with their highly sensitive work.
With a commitment to innovation, Videntifier pushes the boundaries of its solutions. In doing so, they seek to create a safer online environment in which harmful content is effectively dealt with, thus fulfilling their long-held dream of impacting positive change in the battle against illicit online content.
The news and editorial staffs of the New York Daily News had no role in this post’s preparation.
