| Recurring |
multiple_organization |
<Article 64994> discusses a software failure incident related to AI image recognition systems being fooled by small changes in images. The incident described in the article highlights the vulnerability of widely used AI-based image recognition systems to adversarial attacks, where changing just one pixel in an image can lead to misclassification by the neural networks. This issue has been observed in the research conducted by Su Jiawei and colleagues at Kyushu University, as well as by other research groups worldwide, including Anish Athalye from MIT.
The article mentions that many other research groups globally are working on creating "adversarial examples" to expose the weaknesses of these systems. Anish Athalye and his colleagues have demonstrated how a 3D printed turtle can be consistently mislabeled as a rifle by an image classification system, showcasing the susceptibility of these systems to manipulation. The concerns raised by experts like Mr. Su and Mr. Athalye indicate that the problem of adversarial attacks on image recognition systems is not limited to a specific organization but is a broader issue affecting various systems across different organizations and research institutions.
Therefore, based on the information provided in the article, it can be inferred that the software failure incident related to AI image recognition being fooled by small changes in images has not been limited to a single organization but has been observed across multiple organizations and research groups working on neural networks and image recognition systems. |
| Phase (Design/Operation) |
design |
(a) The software failure incident related to the design phase can be seen in the article [64994]. The incident involved AI image recognition systems being fooled by making tiny changes to images, such as altering just one pixel. This vulnerability emerged from the limitations of widely used AI-based image recognition systems developed using deep neural networks. The adversarial examples created by researchers exposed the fragility of these systems, highlighting the design flaws that make them susceptible to misclassification. The incident demonstrates how the design of these systems, based on neural networks, can lead to significant errors in image recognition, even mislabeling objects like a stealth bomber as a dog.
(b) The software failure incident related to the operation phase is not explicitly mentioned in the provided article. |
| Boundary (Internal/External) |
within_system, outside_system |
The software failure incident discussed in the articles relates to the boundary of the system. The failure was due to contributing factors that originated both within and outside the system.
Within the system:
- The incident involved the limitations of AI-based image recognition systems, specifically deep neural networks, which were fooled by small changes in pixels within images [64994].
- Adversarial examples were created to expose the fragility of certain types of recognition software, indicating vulnerabilities within the system itself [64994].
Outside the system:
- The incident was caused by adversarial attacks that exploited the weaknesses of the image recognition systems, indicating external factors influencing the failure [64994].
- The research highlighted that these supposedly smart systems can be easily fooled, raising concerns about potential subversion or attacks using adversarial examples from outside the system [64994]. |
| Nature (Human/Non-human) |
non-human_actions, human_actions |
(a) The software failure incident occurring due to non-human actions:
The article discusses how AI image recognition systems can be fooled by making tiny changes to images, such as altering just one pixel. This manipulation can lead to misclassification of objects by neural networks, highlighting the vulnerability of these systems to adversarial attacks introduced without human participation [64994].
(b) The software failure incident occurring due to human actions:
The article mentions that researchers are actively creating "adversarial" example images to expose the weaknesses of AI-based image recognition systems. Additionally, it is noted that various research groups worldwide are developing adversarial examples to demonstrate the fragility of these systems, indicating that human actions play a role in testing and exploiting the vulnerabilities of the software [64994]. |
| Dimension (Hardware/Software) |
hardware, software |
(a) The software failure incident occurring due to hardware:
- The article discusses how image recognition systems based on deep neural networks can be fooled by making tiny changes to images, such as changing just one pixel. This vulnerability in the systems is attributed to the way neural networks form connections between nodes, similar to nerve cells in a brain. Adversarial examples exploit boundaries between decisions made by the network, allowing for misclassification with minimal perturbations, potentially originating from hardware-related issues [64994].
(b) The software failure incident occurring due to software:
- The vulnerability of image recognition systems to adversarial examples, where small changes in images can lead to misclassification, highlights a software failure issue. The limitations and fragility of certain types of recognition software, particularly those based on deep neural networks, are exposed by the research conducted by various groups worldwide. The inability to easily fix image recognition systems to prevent being fooled in this manner points to underlying software-related challenges [64994]. |
| Objective (Malicious/Non-malicious) |
non-malicious |
(a) The articles do not mention any malicious software failure incidents related to intentional harm caused by humans.
(b) The software failure incident discussed in the articles is non-malicious. It involves the limitations and vulnerabilities of AI-based image recognition systems when exposed to adversarial examples or attacks. These attacks involve making tiny changes to images that can lead the neural networks to misclassify objects, such as mistaking a turtle for a rifle or a cat for a dog. The incident highlights the fragility of certain types of recognition software and the challenges in fixing image recognition systems to prevent being fooled in this manner [64994]. |
| Intent (Poor/Accidental Decisions) |
accidental_decisions |
(a) The software failure incident discussed in the articles is related to accidental_decisions. The incident involved researchers making tiny changes to images to fool widely used AI-based image recognition systems. These changes led to the neural networks wrongly labeling objects, such as mistaking a cat for a dog or a stealth bomber for a dog, due to the vulnerabilities in the systems [64994]. The incident highlights the unintended consequences of these systems being easily fooled by small perturbations in the input data, rather than poor decisions intentionally introduced into the software. |
| Capability (Incompetence/Accidental) |
development_incompetence, accidental |
(a) The software failure incident related to development incompetence is evident in the article. The incident occurred due to the limitations of AI-based image recognition systems when faced with adversarial examples. Researchers made tiny changes to images that caused widely used AI-based image recognition systems, based on deep neural networks, to wrongly label objects. The research highlighted the fragility of certain types of recognition software, indicating a lack of robustness in the systems developed [64994].
(b) The software failure incident related to accidental factors is also present in the article. The vulnerabilities in the image recognition systems were unintentionally exposed by researchers creating adversarial examples to test the systems' weaknesses. The ease with which these supposedly smart systems could be fooled was a cause for concern, even though there had been no examples of malicious attacks in real life. The accidental nature of these vulnerabilities being discovered raises concerns about the security and reliability of such systems [64994]. |
| Duration |
temporary |
From the provided article [64994], the software failure incident related to AI image recognition being fooled by single pixel changes can be categorized as a temporary failure. This temporary failure is due to contributing factors introduced by certain circumstances, such as the specific pixel-based attacks developed by researchers to expose the weaknesses of the image recognition systems. The incident is not a permanent failure as it is caused by specific adversarial examples that exploit vulnerabilities in the deep neural networks used for image recognition. The article mentions ongoing research and efforts to address these vulnerabilities, indicating that the failure is not permanent but rather a result of specific conditions and attacks. |
| Behaviour |
value, other |
(a) crash: The articles do not mention any instances of software crashing where the system loses state and does not perform any of its intended functions.
(b) omission: The articles do not mention any instances of software failure due to the system omitting to perform its intended functions at an instance(s).
(c) timing: The articles do not mention any instances of software failure due to the system performing its intended functions correctly, but too late or too early.
(d) value: The software failure incident discussed in the articles is related to the system performing its intended functions incorrectly. Specifically, the AI-based image recognition systems were fooled by small changes in images, leading to misclassifications such as a cat being mistaken for a dog or a stealth bomber being labeled as a dog [64994].
(e) byzantine: The articles do not mention any instances of software behaving erroneously with inconsistent responses and interactions.
(f) other: The behavior of the software failure incident described in the articles falls under the category of the system performing its intended functions incorrectly due to being fooled by adversarial examples, leading to misclassifications by the AI-based image recognition systems [64994]. |