What is AI detection? Techniques and technologies behind identifying AI-generated content

With the advent of generative AI, producing highly convincing fabricated content has become both easy and affordable. Today, technologies like artificial content generators, image synthesis tools, and deepfake software are being utilized in various contexts. From students circumventing traditional homework methods to malicious actors spreading misinformation regarding wars, political elections, and natural disasters, the applications are vast and often concerning.

This surge in AI-generated content has given rise to a new discipline known as AI detection. This field is dedicated to distinguishing between human-created and machine-generated works. As Kevin Guo, co-founder and CEO of the AI content moderation and detection firm Hive, explained to Built In, “We’re in a new world now. Unfortunately, humans aren’t particularly equipped to identify these creations.” He further noted that the only viable solution to tackle this challenge at scale may ironically involve employing AI technologies themselves.

Understanding AI detection

AI detection refers to the systematic process of determining whether a piece of content—be it text, images, videos, or audio—has been generated by artificial intelligence. Various professionals utilize these detection methods: educators verify the authenticity of students' essays, online moderators sift through social media to identify and remove spam, and journalists work to authenticate media sources and combat the proliferation of fake news.

AI detection typically employs specialized AI-driven software that analyzes numerous patterns and indicators within the content. This can include examining distinct writing styles or identifying visual anomalies that suggest a piece originates from generative AI rather than a human creator.

In essence, these detection tools leverage artificial intelligence to scrutinize other AI outputs. As Alex Cui, co-founder and CTO of the AI detection company GPTZero, articulated, “It’s AIs trying to judge other AIs.” This self-referential dynamic highlights the growing complexity of content creation and verification in a digital landscape increasingly populated by machine-generated works.

As AI technology continues to advance, the importance of AI detection will only increase, emphasizing the need for reliable methods to discern genuine human creativity from its artificial counterparts. This evolving field aims not only to safeguard the integrity of information but also to ensure that the creative endeavors of individuals remain distinguishable in a world where the line between human and machine is becoming increasingly blurred.

Top 5 AI detection tools you should know about

Here are five of the leading AI detection tools available in today’s market:

Hive

Hive provides free detection tools for text, images, videos, and audio. It can identify content generated by several popular AI engines, such as ChatGPT, DALL-E, Midjourney, and Stable Diffusion. Upon inputting media, the tool assesses it and delivers a score reflecting the likelihood of AI generation, along with insights on which AI model likely created it. Besides the detection tool, Hive also offers a variety of moderation tools for text, audio, and visuals, enabling platforms to flag and eliminate spam and harmful content.

Hive

Hive provides free detection tools for text, images, videos, and audio. It can identify content generated by several popular AI engines, such as ChatGPT, DALL-E, Midjourney, and Stable Diffusion. Upon inputting media, the tool assesses it and delivers a score reflecting the likelihood of AI generation, along with insights on which AI model likely created it. Besides the detection tool, Hive also offers a variety of moderation tools for text, audio, and visuals, enabling platforms to flag and eliminate spam and harmful content.

Winston AI

Tailored for educators, publishers, and enterprises, Winston AI’s text detector claims an impressive accuracy of up to 99.98% and is compatible with major language models like GPT-4, Gemini, Llama, and Claude. It can even detect paraphrased content produced by writing assistants and supports multiple languages beyond English. After analyzing the input media, Winston AI indicates the probability that the text is AI-generated and highlights specific sentences believed to be AI-written. It also provides a readability score and a plagiarism checker.

Copyleaks

Copyleaks specializes in detecting AI-generated text by recognizing human writing patterns, only flagging content as potentially AI-produced when deviations from these patterns are found. The tool boasts over 99% accuracy and can identify AI-generated text mixed with human writing. It supports more than 30 languages and works with various AI models, including GPT-4, Gemini, and Claude, as well as newer releases. Additionally, Copyleaks offers a separate tool for identifying AI-generated code and detecting plagiarized or modified code, helping mitigate risks related to licensing and copyright infringement. This tool also assists in safeguarding proprietary code by alerting users to potential breaches or leaks.

GPTZero

GPTZero serves teachers, writers, cybersecurity experts, and recruiters with its AI text detection capabilities. The tool evaluates two key metrics: “burstiness,” which measures variations in sentence structure and length, and “perplexity,” which gauges text unpredictability. These metrics are essential for differentiating between human-written and AI-generated content. After scanning, GPTZero categorizes the document as either AI-generated or human-made, offering a sliding scale of composition. Depending on the scan level selected, users receive detailed insights, including basic sentence breakdowns and color-coded highlights corresponding to specific language models. It also provides evaluations of readability, simplicity, and average sentence length.

How AI detection functions

At a fundamental level, AI detection involves training machine learning models on vast datasets consisting of both human- and AI-generated content. These models analyze patterns that enable them to differentiate between the two. The specific processes vary depending on the tool used and the type of content being evaluated—whether it's text, visual media, or audio.

Image detection

AI-generated images consist of millions of pixels, each containing information about their creation. Image detection tools meticulously analyze these pixels, observing color patterns, sharpness, and any anomalies that may not be typically found in real images, even those subtle enough to escape the human eye.

These tools do not interpret the subject matter of the images, such as faces or objects; instead, they focus solely on the technical attributes of the image.

Text detection

AI text detection tools are typically built on large language models similar to those used in the content generators they aim to identify. They scrutinize various aspects of a text, including word choice, tone, grammar, and other stylistic elements, comparing them to known characteristics of human and AI-generated text to reach a conclusion.

AI-generated texts often follow a "cookie-cutter structure," as noted by Cui from GPTZero. These texts are usually formatted with a straightforward introduction, body, and conclusion or presented as a list of bullet points. Additionally, certain phrases and words frequently used by language models—such as “certainly,” “emphasizing the significance of,” and “plays a crucial role in shaping”—can indicate AI involvement.

Video detection

Video detection tools, like their image counterparts, analyze subtle visual details to ascertain whether content was AI-generated. They also consider the temporal progression of frames, assessing how motion transitions over time. Additionally, these tools evaluate the audio track for signs of altered or synthetic speech, looking for irregularities in voice patterns and background sounds. Common indicators of AI-generated videos include unusual facial movements, sudden quality changes, and discrepancies between audio and visual synchronization.

Audio detection

AI audio detection tools analyze recordings differently from humans. Rather than concentrating on the spoken content, they examine speech patterns, vocal tones, breathing rhythms, background noise, and other acoustic irregularities. These elements serve as valuable indicators in determining whether an audio clip is genuine, manipulated, or entirely AI-generated.

Applications of AI detection tools

Just as generative AI is prevalent in various aspects of life, AI detection tools have also found diverse applications across multiple fields:

• Education: Educators utilize AI text detectors to assess the originality of students’ assignments, ensuring that essays and other written tasks are completed by the students themselves rather than generated by AI tools.
• Social Media: Online moderators leverage AI detection tools to identify and filter out deepfake videos, fake images, and misleading AI-generated articles, striving to uphold credibility and trustworthiness on their platforms.
• Journalism: Journalists employ AI detection tools to verify the authenticity of images, videos, and news articles, aiming to combat the spread of misinformation.
• Cybersecurity: Cybersecurity experts use AI detection tools to recognize and mitigate phishing attacks and other threats that could compromise data security.
• Insurance: Insurance companies implement AI detection tools to identify fraudulent claims based on artificially altered images, ensuring that they do not issue payments for accidents that did not occur.