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World Congress 2025 • Aug 20, 2025 • Session details

Confuse, Obfuscate, Disrupt: Using Adversarial Techniques for Better AI and True Anonymity

David vonThenen

Altering a single pixel can break computer vision, and bad grammar can bypass NLP filters. Discover how to harden your AI pipelines against purposeful adversarial manipulation.

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#1 about 1 min

Manipulating AI models for privacy and anonymity

How manipulating machine learning inputs can defend personal privacy against automated surveillance systems.

#2 about 3 min

Explainable AI and dataset bias detection

Addressing dataset bias and noise is necessary to build ethical and transparent machine learning models.

#3 about 4 min

Measuring data inconsistencies and annotation errors

Identifying labeling errors and adversarial noise requires qualitative observation tools to validate model performance.

#4 about 2 min

Practical motivations for deploying adversarial machine learning inputs

Intentional adversarial inputs act as edge testing for algorithms and provide a safeguard against unwanted tracking.

#5 about 4 min

Natural language processing obfuscation and confusion techniques

Encoding schemes, code-switching, and metaphorical phrases disrupt text processing systems without breaking human comprehension.

#6 about 5 min

Visualizing machine learning inference decisions with Captum

Highlighting specific token attributions in text and images reveals exactly how models arrive at their classifications.

#7 about 4 min

Disrupting sentiment analysis with intentional linguistic errors

Introducing intentional grammar and spelling errors forcefully alters tokenization logic to artificially flip sentiment scores.

#8 about 4 min

Manipulating vision classifiers using single-pixel data poisoning

Placing a single manipulated pixel across training images trains recognition models to predictably misclassify subjects.

#9 about 2 min

Disrupting real-time object detection with adversarial patterns

Wearing clothing printed with specific algorithmic noise prevents live bounding box detection systems from recognizing a human.

#10 about 3 min

Defending machine learning models against adversarial attacks

Applying input normalization and graphic smoothing counters poisoned data streams before they reach the model.

#11 about 2 min

Mitigating vision classifier attacks using Gaussian blur techniques

Executing targeted blur on incoming images neutralizes microscopic adversarial artifacts to restore accurate programmatic classification.

Matching moments

17:05 min

Navigating data privacy boundaries and adversarial model reliability

Alexandra Waldherr · LIVE

3:08 min

Conducting data audits and adversarial testing on models

Toju Duke · WWC 2022

2:01 min

Defending visual artificial intelligence models against data poisoning

Mirko Ross · WWC 2023

4:55 min

Core principles of manipulating artificial intelligence models

Mirko Ross · WWC 2023

1:44 min

Corrupting AI models and software supply chains

Balázs Kiss · WWC 2023

2:02 min

Generating adversarial patterns and defending consumer machine learning models

Mirko Ross · WWC 2023

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