Object detection in underwater environments presents significant challenges due to the inherent limitations of sonar imaging, such as noise, low resolution, lack of texture, and color information. This paper introduces AquaYOLO, an enhanced YOLOv8 version specifically designed to improve object detection accuracy in underwater sonar images. AquaYOLO replaces traditional convolutional layers with a residual block in the backbone network to enhance feature extraction. In addition, we introduce Dynamic Selection Aggregation Module (DSAM) and Context-Aware Feature Selection (CAFS) in the neck network. These modifications allow AquaYOLO to capture intricate details better and reduce feature redundancy, leading to improved performance in underwater object detection tasks. The model is evaluated on two standard underwater sonar datasets, UATD and Marine Debris, demonstrating superior accuracy and robustness compared to baseline models.

Image forgery detection and localization has become a research topic of increasing interest with the enormous spread of manipulated images on the Web. Most previous methods related to image forgery focus on one particular attribute or neglect the importance of multi-scale information. Such approaches are considered not suitable for detecting and locating multi-scale splicing forgeries. This paper presents a novel approach that exploits residual attention and integrates multi-scale local and global information to improve detection accuracy. In the proposed method, we first aggregate multi-level convolutional feature maps extracted by the encoder to enrich the feature representations and improve the ability of the model to locate multi-scale forged areas. Then, we design a residual attention block (RAB) to purify the features, which enhances the response of task-related regions and suppresses noise information. Furthermore, a global feature mining block (GFMB) is proposed to capture the long-range dependencies between different regions of the image, enabling the model to handle complex tampering scenarios effectively. The multi-scale splicing forgery regions are precisely detected and located by utilizing the proposed method. The extensive experiments are conducted on three benchmark datasets, CASIA, COLUMB, and NIST’16. Specifically, our model achieves the F1 score of 84.3%, 87.9%, and 80.8% on CASIA, COLUMB, and NIST’16 test sets, respectively, outperforming state-of-the-art methods.