Advanced image generative models have led to concerns about malicious use, underscoring the necessity for generalizable detection methods. However, existing approaches tend to overfit to domain-specific forgery patterns, while overlooking complementary cues from different domains. Therefore, we introduce DySy-Det (Dynamic Synergy Detector), a novel framework that mines collaborative and robust forgery artifacts from multiple evidence domains. First, DySy-Det fine-tunes a CLIP vision transformer to extract high-level semantics for identifying conceptual inconsistencies, while generating attention maps that pinpoint key discriminative regions. Then, this semantic guidance, in the form of a mask, directs a targeted reconstruction process. By focusing on these salient areas, our approach effectively extracts localized reconstruction errors, thereby filtering out irrelevant background noise. Furthermore, inspired by the intrinsic generative mechanics of diffusion models, we introduce the concept of Reconstruction-Path Consistency (RPC), which quantifies the temporal stability of the denoising trajectory to expose dynamic generative artifacts. We capture this by computing noise alignment scores across multiple timesteps and encode them via a lightweight network. Extensive evaluations on GenImage and UniversalFakeDetect benchmarks demonstrate that DySy-Det outperforms the state-of-the-art detector by 6.14% and 1.57% in mean accuracy, respectively.