Machine learning is at the center of mainstream technology and outperforms classical approaches to handcrafted feature design. Aside from its learning process for artificial feature extraction, it has an end-to-end paradigm from input to output, reaching outstandingly accurate results. However, security concerns about its robustness to malicious and imperceptible perturbations have drawn attention since its prediction can be changed entirely. Salient object detection is a research area where deep convolutional neural networks have proven effective but whose trustworthiness represents a significant issue requiring analysis and solutions to hackers' attacks. Brain programming is a kind of symbolic learning in the vein of good old-fashioned artificial intelligence. This work provides evidence that symbolic learning robustness is crucial in designing reliable visual attention systems since it can withstand even the most intense perturbations. We test this evolutionary computation methodology against several adversarial attacks and noise perturbations using standard databases and a real-world problem of a shorebird called the Snowy Plover portraying a visual attention task. We compare our methodology with five different deep learning approaches, proving that they do not match the symbolic paradigm regarding robustness. All neural networks suffer significant performance losses, while brain programming stands its ground and remains unaffected. Also, by studying the Snowy Plover, we remark on the importance of security in surveillance activities regarding wildlife protection and conservation.

翻译：机器学习是主流技术的核心，其性能超越了传统手工特征设计方法。除了对人工特征提取的学习过程外，它还采用了从输入到输出的端到端范式，从而达到了极其精确的结果。然而，由于恶意和不可察觉的扰动可能完全改变其预测结果，机器学习鲁棒性的安全问题引起了广泛关注。显著目标检测是一个研究领域，深度卷积神经网络在其中表现出了有效性，但其可信度却是一个重要问题，需要分析和应对黑客攻击的解决方案。脑编程是一种符号学习方法，属于传统人工智能的范畴。本研究提供了证据，证明符号学习的鲁棒性在设计可靠的视觉注意系统时至关重要，因为它能够即使是最强烈的扰动。我们利用标准数据库以及一个名为雪鴴的岸鸟的真实问题进行视觉注意任务，测试了这种进化计算方法抵抗多种对抗攻击和噪声扰动的能力。我们将我们的方法与五种不同的深度学习方法进行了比较，证明它们在鲁棒性方面无法与符号范式相媲美。所有神经网络都遭受了显著的性能损失，而脑编程则保持了稳固且不受影响。此外，通过研究雪鴴，我们强调了在野生动物保护与保育的监测活动中安全性的重要性。