Acoustic Inertial Sensor Injection for Physical Image Adversarial Blur
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Solution Overview
Problem
Existing methods for constructing image adversarial examples, whether in digital or physical domains, face challenges in practical applicability and concealment, with digital methods lacking real-world effectiveness and physical methods being overt.
Innovation Solution
A method and system using acoustic waves to affect inertial sensors in machine vision systems, causing image stabilization modules to generate adversarial examples through false readings, simulating camera motion and blur patterns to deceive deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital domain methods are used to generate image adversarial examples by directly modifying pixel values, then adversarial effects on deep learning algorithms are achieved, but practical applicability in real-world systems deteriorates
Solution Approach 1:
The patent introduces acoustic waves as an intermediary medium to transfer adversarial parameters from the digital domain to the physical domain. The acoustic wave modulates inertial sensor readings, which then influence the image stabilization module to produce adversarial effects in real-world images without direct digital manipulation, thus bridging the gap between theoretical adversarial examples and practical applicability.
Solution Approach 2:
The patent replaces direct digital pixel manipulation with a physical acoustic wave-based mechanism. Instead of modifying image data directly, the system uses acoustic waves to physically perturb inertial sensors, which then indirectly affect image capture through the image stabilization system, substituting a mechanical/physical approach for a digital one to enhance practical applicability.
2Adaptability or versatility
If physical domain methods are used to generate image adversarial examples by modifying object appearance or injecting light into cameras, then real-world applicability is achieved, but concealment deteriorates
Solution Approach 1:
The acoustic wave serves as a concealed intermediary that indirectly influences the imaging system. Rather than visibly modifying objects or injecting light into cameras, the acoustic wave subtly modulates inertial sensor readings, making the adversarial attack difficult to detect while maintaining real-world applicability. The intermediary masks the true source of the adversarial effect.
Solution Approach 2:
The patent changes the operating parameters of the inertial sensor by superimposing acoustic wave signals on its readings. This parameter manipulation occurs in the sensor data domain rather than in the physical object or optical path, allowing the attack to remain concealed while still achieving real-world adversarial effects through the image stabilization system.
3Difficulty of detecting and measuring
If acoustic wave-based inertial sensor reading injection is used to generate image adversarial examples, then concealment is improved, but system complexity deteriorates
Solution Approach 1:
The acoustic wave acts as a simple intermediary that naturally interfaces with the inertial sensor without requiring complex hardware modifications. By using sound waves, which are a fundamental physical phenomenon, the system achieves concealment while avoiding the need for sophisticated injection devices, thus managing system complexity.
Solution Approach 2:
The acoustic wave mechanism serves multiple functions: it conveys adversarial parameters, modulates sensor readings, and remains difficult to detect. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while achieving high concealment through a single versatile approach.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides practical and concealed image adversarial examples in the physical world, enhancing applicability and security in machine vision systems.
Implementation Method 1
injecting the adversarial parameters into an inertial sensor of the target machine vision system in a manner of an acoustic wave
Data Source
AI summary
The disclosure discloses a method and a system for generating image adversarial examples based on an acoustic wave. The method includes: acquiring an image containing a target object or a target scene; generating simulated image examples for the acquired image, wherein the simulated image examples have adversarial effects on a deep learning algorithm in a target machine vision system; optimizing the generated simulated image examples to obtain an optimal adversarial example and corresponding adversarial parameters; and injecting the adversarial parameters into an inertial sensor of the target machine vision system in a manner of an acoustic wave, such that the adversarial parameters are used as sensor readings that will cause an image stabilization module in the target machine vision system to operate to generate particular blurry patterns in a generated real-world image so as to generate an image adversarial example in a physical world.