Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Abstract: Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call "fooling images" (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.
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Synopsis
Overview
- Keywords: Deep Neural Networks, Image Recognition, Fooling Images, Evolutionary Algorithms, Confidence Predictions
- Objective: Investigate how deep neural networks can be easily misled into making high-confidence predictions on images that are unrecognizable to humans.
- Hypothesis: Deep neural networks can be tricked into classifying unrecognizable images with high confidence, revealing significant differences between human and machine perception.
Background
Preliminary Theories:
- Discriminative vs. Generative Models: Discriminative models, like DNNs, learn decision boundaries for classification, while generative models learn the distribution of data within classes.
- Evolutionary Algorithms: Techniques that mimic natural selection to optimize solutions, applied here to generate images that DNNs classify confidently.
- Gradient Ascent: An optimization technique used to maximize the output of a neural network by adjusting input images based on the gradient of the output probabilities.
Prior Research:
- Szegedy et al. (2014): Demonstrated that imperceptible perturbations to images could lead to misclassification by DNNs, highlighting vulnerabilities in neural networks.
- Generative Adversarial Networks (GANs): Developed to create realistic images, showing the potential for generative models to produce images that resemble training data.
- Transfer Learning: Explored how features learned in one context can be applied to different tasks, relevant for understanding DNN generalization.
Methodology
Key Ideas:
- Image Generation: Utilized evolutionary algorithms and gradient ascent to create images that DNNs classify with high confidence.
- Convolutional Neural Networks: Employed well-known architectures like AlexNet and LeNet trained on ImageNet and MNIST datasets, respectively.
- CPPN Encoding: Used Compositional Pattern Producing Networks to evolve images that exhibit regular patterns, optimizing for DNN confidence.
Experiments:
- Ablation Studies: Tested the robustness of DNNs against retraining with fooling images, revealing that new fooling images could still be generated.
- Benchmarking: Evaluated the confidence scores of generated images across multiple runs and generations, comparing performance between different DNN architectures.
Implications: The findings suggest that DNNs have significant blind spots in their recognition capabilities, raising concerns about their reliability in critical applications.
Findings
Outcomes:
- DNNs classified images that were completely unrecognizable to humans with confidence scores exceeding 99%.
- Evolutionary algorithms produced a diverse range of fooling images, indicating that DNNs rely on specific discriminative features rather than holistic understanding.
- Retraining DNNs with fooling images did not prevent the emergence of new fooling images, demonstrating a persistent vulnerability.
Significance: This research challenges the assumption that high confidence in predictions correlates with accurate recognition, emphasizing the need for improved understanding of DNNs' generalization capabilities.
Future Work: Investigating the robustness of generative models against similar fooling techniques and exploring the implications for security in systems relying on DNNs.
Potential Impact: Addressing these vulnerabilities could lead to more reliable DNN applications in safety-critical areas, such as autonomous vehicles and security systems.