Image processing tasks are rapidly migrating to cloud and multi-party collaborative environments. However, directly processing plaintext image data on cloud servers easily leads to the leakage of sensitive information in images and is difficult to resist malicious attacks such as tampering, thus failing to guarantee data integrity and service reliability. To address these challenges, this study proposes MalOD, an object detection inference framework for environments with malicious adversaries. MalOD is a framework to achieve secure object detection under malicious adversaries. MalOD constructs an encrypted feature pyramid network (SecFPN) to perform multi-level feature extraction on encrypted images. Based on these multi-level cipher text features, a secure region proposal network (SecRPN) and a secure region of interest align (SecRoIA) module are designed to achieve secure object detection. By leveraging replicated secret sharing (RSS), a series of secure computation primitives are designed, including a secure ceiling function, secure bilinear interpolation, and secure nearest-neighbor interpolation. These primitives provide the underlying support for SecFPN, SecRPN, and SecRoIA, ensuring the efficiency and accuracy of the detection process under malicious adversaries. The correctness and security of MalOD are proved, and its performance is evaluated on the COCO 2017 and Pascal VOC 2012 datasets. Experimental results show that MalOD achieves high object detection accuracy while meeting strict security requirements. In particular, when the intersection over union (IoU) threshold is 0.5, the average precision on the COCO subset decreases by only 0.113 compared with plaintext detection. This study provides theoretical and practical support for privacy-preserving image processing under malicious environments and is particularly suitable for untrusted cloud computing and multi-party collaboration scenarios.