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There were 18.4 million reports of child pornography on the internet last year, which included 45 million images and videos of child sexual abuse, according to an investigation by the New York Times.
Why it matters: Despite tech companies', law enforcement agencies' and legislators' best efforts to prevent the spread of child pornography, the number of reports has exploded over the last 3 decades as technology makes abusive images more accessible and easier to spread.
How it works: The number of child abuse reports has increased in tandem with the rise of encryption technology, specifically encrypted messaging apps.
- Facebook announced in March plans to encrypt Messenger, which was responsible for nearly 12 million of the 18.4 million child pornography reports last year, according to the Times.
- Pedophiles use these apps to swap or sell their collections of images and videos.
- Increasingly, criminals are using encryption technologies to protect websites and imagery from investigators.
By the numbers: In 1998, there were more than 3,000 reports of child sexual abuse imagery. "In 2014, that number surpassed 1 million for the first time," per the Times.
Context: Congress in 2008 passed the PROTECT Our Children Act, which foresaw many aspects of the proliferation of child pornography. The Times, however, found that the federal government had not fulfilled major aspects of the legislation.
The big picture: The problem is global. Most of the images found last year originated in countries outside the U.S., but the problem is compounded by Silicon Valley, which hosts companies accused of facilitating the spread of child abuse imagery.
- Yes, but: Those same companies are also the leaders in reporting child pornography to the authorities.
What's next: A paper recently published by the National Center for Missing and Exploited Children suggested that law enforcement agencies and platform operators like Google, Microsoft, Facebook and Twitter may be able to develop software that automatically detects child pornography using machine learning.
Go deeper: Read the full New York Times investigation