<p>Object detection has advanced significantly, becoming essential in applications of internet of things (IoT) such as autonomous vehicle, mobile devices, smart home and intelligent transportation system. To overcome the performance limitations of single object detection models, these models are often combined with preprocessing techniques like multi-focus image fusion to enhance detection capabilities. Despite these enhancements, the robustness of such integrated systems against adversarial attacks remains a significant challenge, as prior research has primarily focused on individual detection models rather than the entire detection system. We propose UMOG (A universal adversarial example generation method for object detection system coupled with multi-focus image fusion model) method, a novel approach specifically designed to exploit vulnerabilities in object detection systems that incorporate multi-focus image fusion. UMOG hinges on two synergistic blocks: CAEGM crafts perturbations against detection outputs and iteratively audits them to keep single and fused models fooled, while NRM clamps the noise to Canny-dilated edges for minimal visibility. By exploiting the high-frequency-preserving trait of multi-focus fusion, the same perturbation slips through fusion and fools downstream detectors, exposing a neglected weak spot that transfers across fusion variants and detection backbones. Empirical tests show that such universal adversarial examples maintain high attack success rates against both standalone detectors and fusion-equipped pipelines, highlighting the urgent need for stronger defenses in real-world vision systems.</p>