異常資料偵測在神經網路的安全議題上，是一個值得探討的方向。一旦訓練好的神經網路遇上了無法識別的資料型態，就極有可能發生錯誤的判斷，導致無可挽回的後果，像是自動駕駛以及醫學診斷系統就是其中經典的例子。因此，一個有效的分類器不只應該要能準確的識別原先的類別項目，也要能辨識出不屬於他認知範圍的異常資料。
本論文提出一個基於特徵金字塔網路之異常圖像偵測系統。相比起其他異常檢測系統的單一輸入單一預測值，我們將系統結合了特徵金字塔網路，因此針對單一影像輸入，可以輸出多尺度的預測值，透過統整多尺度的預測值，有效地讓系統準確度提升。實驗結果顯示此系統不僅可以保留原先任務需求，且在多個視覺資料集上皆顯示辨識效果有所提升。

Anomaly data detection is a direction worthy of discussion on the security issue of neural networks. Once an unrecognized data type is input into a trained neural network which is very likely to make wrong judgments, and it will lead to an irreversible consequences, such as autonomous driving and medical diagnosis systems are classic examples.
In this paper, we propose an out-of-distribution image detection system based on feature pyramid network. Compared with the single input and single prediction of other out-of-distribution detection systems, we combine the system with the feature pyramid network, so for a single image input, multi-scale prediction would be output. Experimental results show that the system not only retains the original task, but also improves the recognition accuracy on multiple visual datasets.

[1] A. v. d. Oord et al., "Wavenet: A generative model for raw audio," arXiv preprint arXiv:1609.03499, 2016.
[2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, 2012.
[3] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[4] A. Geiger, P. Lenz, and R. Urtasun, "Are we ready for autonomous driving? the kitti vision benchmark suite," in 2012 IEEE conference on computer vision and pattern recognition, 2012: IEEE, pp. 3354-3361.
[5] M. Jamshidi et al., "Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment," Ieee Access, vol. 8, pp. 109581-109595, 2020.
[6] H. Idrees, M. Shah, and R. Surette, "Enhancing camera surveillance using computer vision: a research note," Policing: An International Journal, 2018.
[7] I. J. Goodfellow, J. Shlens, and C. Szegedy, "Explaining and harnessing adversarial examples," arXiv preprint arXiv:1412.6572, 2014.
[8] K. Lee, K. Lee, H. Lee, and J. Shin, "A simple unified framework for detecting out-of-distribution samples and adversarial attacks," Advances in neural information processing systems, vol. 31, 2018.
[9] L. Ruff et al., "Deep one-class classification," in International conference on machine learning, 2018: PMLR, pp. 4393-4402.
[10] D. Hendrycks, M. Mazeika, and T. Dietterich, "Deep anomaly detection with outlier exposure," arXiv preprint arXiv:1812.04606, 2018.
[11] S. Liang, Y. Li, and R. Srikant, "Enhancing the reliability of out-of-distribution image detection in neural networks," arXiv preprint arXiv:1706.02690, 2017.
[12] S. Vernekar et al., "Analysis of confident-classifiers for out-of-distribution detection," arXiv preprint arXiv:1904.12220, 2019.
[13] V. Abdelzad, K. Czarnecki, R. Salay, T. Denounden, S. Vernekar, and B. Phan, "Detecting out-of-distribution inputs in deep neural networks using an early-layer output," arXiv preprint arXiv:1910.10307, 2019.
[14] D. Hendrycks and K. Gimpel, "A baseline for detecting misclassified and out-of-distribution examples in neural networks," arXiv preprint arXiv:1610.02136, 2016.
[15] N. Akhtar and A. Mian, "Threat of adversarial attacks on deep learning in computer vision: A survey," Ieee Access, vol. 6, pp. 14410-14430, 2018.
[16] P. Bergmann, M. Fauser, D. Sattlegger, and C. Steger, "MVTec AD--A comprehensive real-world dataset for unsupervised anomaly detection," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 9592-9600.
[17] L.-J. Li and L. Fei-Fei, "Optimol: automatic online picture collection via incremental model learning," International journal of computer vision, vol. 88, no. 2, pp. 147-168, 2010.
[18] S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, "A survey of deep learning techniques for autonomous driving," Journal of Field Robotics, vol. 37, no. 3, pp. 362-386, 2020.
[19] I. Evtimov et al., "Robust physical-world attacks on machine learning models," arXiv preprint arXiv:1707.08945, vol. 2, no. 3, p. 4, 2017.
[20] M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter, "Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition," in Proceedings of the 2016 acm sigsac conference on computer and communications security, 2016, pp. 1528-1540.
[21] S. Basu and M. Meckesheimer, "Automatic outlier detection for time series: an application to sensor data," Knowledge and Information Systems, vol. 11, no. 2, pp. 137-154, 2007.
[22] T. Xiao, C. Zhang, and H. Zha, "Learning to detect anomalies in surveillance video," IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1477-1481, 2015.
[23] Z. Chen, C. K. Yeo, B. S. Lee, and C. T. Lau, "Autoencoder-based network anomaly detection," in 2018 Wireless telecommunications symposium (WTS), 2018: IEEE, pp. 1-5.
[24] J. Guo, G. Liu, Y. Zuo, and J. Wu, "An anomaly detection framework based on autoencoder and nearest neighbor," in 2018 15th International Conference on Service Systems and Service Management (ICSSSM), 2018: IEEE, pp. 1-6.
[25] T. Denouden, R. Salay, K. Czarnecki, V. Abdelzad, B. Phan, and S. Vernekar, "Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance," arXiv preprint arXiv:1812.02765, 2018.
[26] K. Lee, H. Lee, K. Lee, and J. Shin, "Training confidence-calibrated classifiers for detecting out-of-distribution samples," arXiv preprint arXiv:1711.09325, 2017.
[27] D. P. Kingma and M. Welling, "Auto-encoding variational bayes," arXiv preprint arXiv:1312.6114, 2013.
[28] I. Goodfellow et al., "Generative adversarial nets," Advances in neural information processing systems, vol. 27, 2014.
[29] S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms for mining outliers from large data sets," in Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000, pp. 427-438.
[30] C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, "On calibration of modern neural networks," in International conference on machine learning, 2017: PMLR, pp. 1321-1330.
[31] W. Wang, A. Wang, A. Tamar, X. Chen, and P. Abbeel, "Safer classification by synthesis," arXiv preprint arXiv:1711.08534, 2017.
[32] J. Yang, K. Zhou, Y. Li, and Z. Liu, "Generalized out-of-distribution detection: A survey," arXiv preprint arXiv:2110.11334, 2021.
[33] E. H. Adelson, C. H. Anderson, J. R. Bergen, P. J. Burt, and J. M. Ogden, "Pyramid methods in image processing," RCA engineer, vol. 29, no. 6, pp. 33-41, 1984.
[34] K. He, X. Zhang, S. Ren, and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904-1916, 2015.
[35] R. Girshick, "Fast r-cnn," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440-1448.
[36] S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, vol. 28, 2015.
[37] W. Liu et al., "Ssd: Single shot multibox detector," in European conference on computer vision, 2016: Springer, pp. 21-37.
[38] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117-2125.
[39] K. Sohn et al., "Fixmatch: Simplifying semi-supervised learning with consistency and confidence," Advances in Neural Information Processing Systems, vol. 33, pp. 596-608, 2020.
[40] E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, "Randaugment: Practical automated data augmentation with a reduced search space," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 702-703.
[41] S. Zagoruyko and N. Komodakis, "Wide residual networks," arXiv preprint arXiv:1605.07146, 2016.
[42] J. Davis and M. Goadrich, "The relationship between Precision-Recall and ROC curves," in Proceedings of the 23rd international conference on Machine learning, 2006, pp. 233-240.
[43] A. Coates, A. Ng, and H. Lee, "An analysis of single-layer networks in unsupervised feature learning," in Proceedings of the fourteenth international conference on artificial intelligence and statistics, 2011: JMLR Workshop and Conference Proceedings, pp. 215-223.
[44] Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, "Reading digits in natural images with unsupervised feature learning," 2011.
[45] I. Loshchilov and F. Hutter, "Sgdr: Stochastic gradient descent with warm restarts," arXiv preprint arXiv:1608.03983, 2016.