But that also means trusted AI like NSFW AI can be defeated under edge cases. It is mainly constructed from convolutional neural networks (CNN), natural language processing (NLP) and image recognition methods to classify explicit content through visual, as well textual data. But adversarial attacks showed that AI tools are not immune to attack and they can be easily deceived by altering some parts of an image or text-led. For example, some studies have shown that slight changes (or even distortions) to pixels can reduce accuracy by upward of 30%, allowing explicit content to slip through the cracks.
For example, one of the most common ways that NSFW AI is fooled by using adversarial noise—a simple procedure whereby attackers add small distortions to an image which are imperceptible to a human observer but mess up how AIs process what they see In 2020, researchers at MIT discovered that some neural networks can make mistakes on perturbed images as much as 45% of the time with just small pixel-wise adjustments. This shows how easy these modifications can fool even advanced AI, which is why NSFW technology needs constant improvements.
These kinds of misinformation tactics highlight exactly why platforms like Facebook and Instagram — as well as TikTok, which is investing millions a year more than it makes to improve its AI in the face of determined adversaries. These harsh realities guide companies to ensure their AI´s robustness against adversarial manipulation techniques, as the safety of users should not be at risk due these weaknesses. The cost of retraining for all different types of major platforms after a big breach is typically $100,000 or more on average — and this number accurately represents the financial costs to companies that must adhere to secure online communities.
This is something almost every AI and cybersecurity practitioner identify as a known problem with machine learning. The consistent battle that AI developers wage against those who would use it for nefarious purposes was summed up by Elon Musk, “AI needs constant adaptation; today’s strength may be tomorrow weakness.” To thwart such adversarial tactics, developers have armed NSFW AI with a diversity of datasets and do their best to cover ambiguous content types as well so the system learns how try real pattern matching even if hidden through distortions.
To make things more difficult, deepfake technology becomes another way to trick AI NSFW. These involve the use of GANs (Generative Adversarial Networks) in creating artificially true synthetic media, with deepfakes being a useful application. Deepfakes are particularly insidious when combined with explicit content as the compromised media can be — especially if it is being modified to avoid detection by moderations systems. As of today, platforms are battling deepfakes with machine learning based algorithms that help differentiate between real content and AI (around 94% accurate to date which is expected early days and will get better as the technology mature)
NSFW AI gives resources to those curious about the growth of NSFW AIs as they improve, please view nsfw ai for info on the most recent progress in artificial intelligence technology. It is a never-ending battle of content moderation, preventing heinous stuff from seeping through and evolving manipulation techniques that target the glaring weaknesses of NSFW AI.