<p>Deepfake detectors achieve high accuracy on clean benchmarks but are highly vulnerable to adversarial perturbations, often dropping below 20% area under the curve (AUC) under unseen attacks. To bridge this gap without retraining existing models, we propose GTCP, a plug-and-play general trace compensation purifier that suppresses adversarial noise while compensating for essential forensic traces. GTCP operates in two stages. First, a cross-domain consistency learner (CDCL) employs supervised contrastive learning and an invariant risk minimization (IRM) inspired alignment to remove representation bias across real and forged domains, yielding a unified embedding where samples form compact, well-separated clusters under a single global decision boundary. A multi-domain forensic traces dictionary (MFTD) is then constructed over this embedding, comprising orthogonal orientation-selective “trace atoms” that precisely encode high-frequency forensic textures specific to each forgery type. Second, a collaborative reconstruction mechanism integrates the representation navigator (RN) with a novel calibration loss to dynamically select and combine the most semantically relevant trace atoms from MFTD, ensuring accurate high-frequency traces compensation. Fusing these restored details with denoised low-frequency content yields purified images that retain clean-input accuracy and substantially boost robustness against both white-box and black-box attacks. Extensive evaluations on FaceForensics++, Celeb-DF, deepfake detection challenge dataset (DFDC), and multiple detection backbones show that GTCP improves adversarial AUC by more than 60%, achieving a new state-of-the-art in robust deepfake detection.</p>