# Image Disguising: The Future of Privacy in Medical AI? > Source: > Published: 2026-07-13 05:23:57+00:00 # Image Disguising: The Future of Privacy in Medical AI? Image disguising tech promises privacy for medical AI but struggles with complex tasks. Are we prioritizing privacy at the cost of functionality? medical AI, the tension between utility and privacy is a tightrope walk. Cloud-based [deep learning](/glossary/deep-learning) offers immense potential for analyzing medical images. Yet, the privacy concerns it raises are equally immense. Enter image disguising, a technology that's trying to solve this conundrum by transforming sensitive medical images into unintelligible forms, all while keeping the data intact for AI processes. ## The Players: DisguisedNets and NeuraCrypt Researchers have been evaluating methods like DisguisedNets and NeuraCrypt across four different datasets, focusing on tasks like [classification](/glossary/classification) and semantic segmentation. It's a bold move because these tasks require precision. If you're disguising the images, you better make sure you're not losing critical information in the process. But let's face it, the results were a mixed bag. While these methods managed to preserve utility for simpler tasks like classification, they faltered when it came to complex tasks like dense semantic segmentation. ## Method Wars: RMT vs. AES-based Disguising One method that stood out was Randomized Multidimensional Transformation (RMT). This approach struck the best balance between performance and security. Meanwhile, the AES-based disguising method, though secure, severely impacted the utility of the data. It's a classic trade-off scenario. If you want bulletproof security, be prepared to sacrifice some efficiency. But let's not forget, data privacy isn't a crime. It's a prerequisite for freedom. ## The Reconstruction Threat Reconstruction attacks, especially [regression](/glossary/regression)-based ones, pose a serious threat to disguised images. However, the study found these attacks to be less effective on medical images compared to natural images. Yet, does this mean we're out of the woods? Not really. The model remembers everything you typed. That should worry you sensitive data like medical records. So, why should you care? If it's not private by default, it's surveillance by design. We can't turn a blind eye to the implications of using weak disguising methods. The healthcare industry can't afford to gamble with privacy for the sake of AI advancements. After all, opt-in privacy is no privacy at all. It's time to ask ourselves: Are we willing to compromise functionality for privacy, or can we've both? Get AI news in your inbox Daily digest of what matters in AI. ## Key Terms Explained [Classification](/glossary/classification) A machine learning task where the model assigns input data to predefined categories. [Deep Learning](/glossary/deep-learning) A subset of machine learning that uses neural networks with many layers (hence 'deep') to learn complex patterns from large amounts of data. [Regression](/glossary/regression) A machine learning task where the model predicts a continuous numerical value.