How to protect your photos from AI

ALSO: Nobel-winning economist warns unregulated AI could worsen inequality

Author

Zain Kahn
August 02, 2023

Read time: 2.5 minutes

Welcome back, superhuman

It’s an academic day in AI as MIT comes up with a new way to protect your photos from AI. While a Nobel prize-winning economist is worried about AI’s impact on inequality.

You’ll also get a roundup of the most interesting AI research I’ve found recently. Let’s cut to the chase.

 TODAY’S MENU

  • The Big Insight: MIT tool helps you protect photos from AI

  • 5 AI tools to supercharge your productivity

  • Research Roundup: The evolving behavior of LLMs, catching deepfakes, and identifying tumors

  • AI-Generated Images: Musical houses

TODAY IN AI & TECH  

  • Profitable at last: Uber posted its first operating profit with free cash flow of over $1 billion, after racking up $31.5 billion in losses.

  • Chip war: Chip manufacturer AMD plans to launch its AI chip by end of year to compete with current market leader Nvidia.

  • Short and Sweet: YouTube testing AI-generated summaries with select users to help them find videos to watch.

  • Robo-doctor: Amazon rolls out virtual healthcare service across the US to help users get treatment for common illnesses.

  • New horizons: Foxconn to invest $600 million in India to produce iPhone components.

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THE BIG INSIGHT

How to protect your photos from AI

Image source: malwarebytes.com

AI tools like Midjourney and Dall-E have been blowing our minds over the last year with their hyper-realistic image generations. In fact, many of the images you see in this newsletter are generated with Midjourney - we love it!

But with great upside usually comes grave downside. There’s growing concern about the misuse of AI models that generate images, especially the potential for bad actors to twist your images into something sinister or unwanted.

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) is coming to our (partial) rescue with an AI tool called PhotoGuard to protect images from malicious editing.

PhotoGuard works by changing certain pixels to make an image imperceptible, making it invisible to the human eye but detectable by computer.

The technique that PhotoGuard uses is a distinct two "attack" method to create perturbations:

  • Encoder attack changes how the AI program sees and understands the picture by making it undetectable.

  • Diffusion attack tricks the AI into seeing a picture as something different, so any changes it makes don't make sense.

Here’s a video demo:

While the tool makes it significantly harder to change pictures, it's not yet perfect. Some might try to bypass its protections by reverse engineering the algorithm. The tool also needs some further refinement before its released to the general public.

Other Insights

Unregulated AI will worsen inequality, warns Nobel-winning economist Joseph Stiglitz

Nobel-winning economist warns that unregulated AI could exacerbate inequality by automating jobs and concentrating wealth. Read more → 

Should an AI bot making $1mn really be the next Turing test

DeepMind co-founder proposes a new AI benchmark: making $1 million on a retail web platform with a $100,000 investment. Read more → (requires FT subscription)

5 AI Tools to Supercharge Your Productivity

Hireguide: AI enhanced structured interviews and notes that helps you conduct interviews and reduce hiring time.

Relay: AI-powered portfolio analyzer. Extract key details from investment documents and company updates.

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RESEARCH ROUNDUP 

The most interesting AI research we found this week

A leap towards generalist biomedical AI

Google has created a new medical AI tool called Med-PaLM M to understand and analyze different types of data. It encodes and interprets diverse biomedical data, outperforming specialist models in tasks like medical question answering, image interpretation, and genomic variant calling. Read more →

Evolving behavior of language models: A case study on GPT-3.5 and GPT-4

The study scrutinizes GPT-3.5 and GPT-4's performance over time, emphasizing the necessity for continuous quality monitoring and the challenges in integrating these models into larger workflows. Read more →

A new framework to detect deepfakes

The paper presents Detect Any Deepfakes, a framework using the Segment Anything Model (SAM) for detecting and localizing face forgery. It includes a Multiscale Adapter and a Reconstruction Guided Attention module to capture forgery contexts, outperforming existing methods on three benchmark datasets. Read more →

Brain tumor detection using deep neural networks

This paper explores the use of deep neural networks for brain tumor detection from MRI images, achieving high accuracy. It incorporates explainable AI approaches and a cost-sensitive neural network for imbalanced datasets. Read more → 

AI-GENERATED IMAGES

Musical houses

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