# Exploiting Alpha Transparency In Language And Vision-Based AI Systems

arXiv: https://arxiv.org/abs/2402.09671 · HTML: https://mlsanity.com/paper/2402.09671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 15 Feb 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [On the Robustness of Vision Transformers to Adversarial Examples](https://mlsanity.com/paper/2104.02610/) (2021)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks](https://mlsanity.com/paper/2006.12557/) (2020)
- [Towards Transferable Adversarial Attacks on Vision Transformers](https://mlsanity.com/paper/2109.04176/) (2021)
- [CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning](https://mlsanity.com/paper/2303.03323/) (2023)
- [Enhancing CLIP with GPT-4: Harnessing Visual Descriptions as Prompts](https://mlsanity.com/paper/2307.11661/) (2023)
- [Data Poisoning Won't Save You From Facial Recognition](https://mlsanity.com/paper/2106.14851/) (2021)
- [Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles](https://mlsanity.com/paper/2108.02940/) (2021)
- [Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers](https://mlsanity.com/paper/2004.11514/) (2020)
- [What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning](https://mlsanity.com/paper/2102.13624/) (2021)
- [Simple Transparent Adversarial Examples](https://mlsanity.com/paper/2105.09685/) (2021)
- [Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks](https://mlsanity.com/paper/2005.00656/) (2020)
- [Transparency Attacks: How Imperceptible Image Layers Can Fool AI Perception](https://mlsanity.com/paper/2401.15817/) (2024)

## Abstract (arXiv)

This investigation reveals a novel exploit derived from PNG image file formats, specifically their alpha transparency layer, and its potential to fool multiple AI vision systems. Our method uses this alpha layer as a clandestine channel invisible to human observers but fully actionable by AI image processors. The scope tested for the vulnerability spans representative vision systems from Apple, Microsoft, Google, Salesforce, Nvidia, and Facebook, highlighting the attack's potential breadth. This vulnerability challenges the security protocols of existing and fielded vision systems, from medical imaging to autonomous driving technologies. Our experiments demonstrate that the affected systems, which rely on convolutional neural networks or the latest multimodal language models, cannot quickly mitigate these vulnerabilities through simple patches or updates. Instead, they require retraining and architectural changes, indicating a persistent hole in multimodal technologies without some future adversarial hardening against such vision-language exploits.