Recently, the Image Captioning (IC) technique has been widely applied to describe a given image in text form. However, IC systems can still produce incorrect captions and lead to misunderstandings. To tackle this problem, several methods have been proposed to test the IC systems. However, these approaches still rely on pre-annotated information and hence cannot really alleviate the oracle problem in the testing. Besides, they adopt AIGC techniques to create follow-up test images that may generate unrealistic images as test cases, which leads to meaningless testing results. Thirdly, existing methods have various restrictions on the eligibility of source test cases, and hence cannot fully utilize the given images to perform testing. To tackle these issues, we propose REIC, which conducts metamorphic testing for IC systems with reduction-based transformations. Instead of relying on the pre-annotated information, we introduce a localization method to align the described objects in the caption with the corresponding objects in the test image and check whether each object in the caption retains or disappears after transformation. REIC does not artificially manipulate any objects and hence can effectively avoid generating unreal follow-up images. Besides, it eliminates the restrictions in the metamorphic transformation process, as well as decreases the ambiguity, and boosts the diversity among the follow-up test cases, which consequently enables testing to be performed on any test image, and reveals more distinct valid violations. Experimental results demonstrate that REIC can sufficiently leverage provided test images to generate follow-up cases of good reality, and effectively detect a great number of distinct violations, without the need for any pre-annotated information.

翻译：近年来，图像描述技术被广泛应用于以文本形式描述给定图像。然而，图像描述系统仍可能生成错误描述并导致误解。为解决该问题，已有多种方法被提出来测试图像描述系统。但这些方法仍依赖预标注信息，无法真正缓解测试中的预言困境。此外，这些方法采用AIGC技术生成后续测试图像，可能会产生不真实的图像测试用例，导致无效测试结果。再者，现有方法对源测试用例的适用性存在诸多限制，无法充分利用给定图像进行测试。针对上述问题，我们提出REIC方法，通过基于归约的变换对图像描述系统进行蜕变测试。该方法不依赖预标注信息，而是引入定位技术将描述语句中的对象与测试图像中的对应目标对齐，并检测每个描述对象在变换后是否保留或消失。REIC不人为操控任何对象，能有效避免生成不真实的后续图像。同时，该方法消除了蜕变变换过程中的限制条件，降低了歧义性，提升了后续测试用例的多样性，从而可对任意测试图像执行测试，并发现更多有效的违规行为。实验结果表明，REIC能在无需任何预标注信息的情况下，充分运用提供的测试图像生成具有良好真实性的后续用例，并有效检测出大量不同的违规行为。