## 1 Introduction
[14] developed a video forgery detection system using a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model, integrating facial feature extraction via a Multi-task Cascaded Convolutional Neural Network (MTCNN) and Extreme Inception (Xception) with temporal feature capture through Long Short-Term Memory (LSTM). Although this model performed well on the Google Deepfake AI dataset [15], it struggled with issues such as color inconsistency. In addition, advancements include those by [PERSON] et al. [16], who mixed sparse coding with deep learning for super-resolution image restoration. Despite achieving high restoration accuracy and subjective quality, their method incurs significant computational overhead and has limited applicability. [PERSON] et al. [17] enhanced local matching techniques by integrating multi-scale Local Binary Pattern (LBP) features with time fusion, demonstrating success in low-resolution surveillance video face recognition. However, its accuracy declines under extremely low resolution and complex backgrounds. [PERSON] et al. [18] introduced the blur-ASR method, which leverages sparse representation with dictionaries tailored to various blur levels to improve low-resolution image recognition. While effective, its performance is highly dependent on dataset representativeness, limiting its applicability in real-world scenarios. [PERSON] et al. [19] introduced an adaptive feature extraction technique effective for blurred face images, though it struggles with severe blur and high computational complexity. [PERSON] et al. [20] proposed a two-stage pipeline using the pix2 pix network for low-resolution face image enhancement, which is coupled with a multi-quality fusion network to improve recognition performance. Despite its strengths, the method is limited in restoring extremely low-quality data and suffers from high computational complexity. [PERSON] et al. [21] presented a lightweight convolutional model combining multi-resolution feature fusion and spatial attention modules, outperforming traditional CNNs on the Lock3 DFace dataset [22]. Nevertheless, its ability to handle extremely low-quality data remains constrained. Many existing methods focus on a single technique, such as super-resolution restoration or feature extraction, and often exhibit limited performance on extremely low-quality images or incur significant computational overhead. Consequently, current research has yet to provide a comprehensive solution for addressing the combined challenges of noise, blur, and low resolution in low-quality forged images. To address these limitations, this paper proposes the YOLOv9-ARC algorithm, which significantly enhances the detection performance of low-quality forged images through the introduction of two key components: the Adaptive Kernel Convolution (AKConv) module and the Convolutional Block Attention Module (CBAM). Unlike previous approaches, the core innovation of this work lies in the synergistic integration of dynamically adjustable convolution kernels (AKConv) and multi-level attention mechanisms (CBAM), which optimize feature extraction and feature representation, respectively. The AKConv module dynamically adjusts the size of the convolution kernel, enabling the model to effectively handle low resolution, noise, and blur, thereby enhancing its feature extraction capability on low-quality images. Simultaneously, the CBAM mechanism dynamically recalibrates feature importance across both channel and spatial dimensions, amplifying critical regions while suppressing noise and irrelevant features. Compared to existing techniques that rely solely on feature extraction or super-resolution restoration, the proposed approach combines adaptive convolution kernel adjustment with attention-guided feature optimization. This dual strategy not only improves the model's adaptability to low-quality images but also achieves higher accuracy in addressing challenges such as low resolution and noise.