Efficient Document Tampering Localization with Multi-Level Discrepancy Features and Unified DCT–Quantization Embedding

Localizing document tampering is extremely challenging, asmanipulations are crafted to appear visually consistent and often leaveonly subtle traces that are nearly invisible to the human eye. In priorwork, evaluation has been largely dominated by synthetic benchmarksthat closely match the training distribution, and methods have shownsteady progress under this setting. However, these gains often translatepoorly to human-made forgeries and to cross-domain evaluation, whereboth the source documents and the tampering pipeline can change, lead-ing to a distribution shift. In addition, since the introduction of the Fre-quency Perception Head for the discrete cosine transform (DCT) modal-ity, it has become a standard choice, and subsequent work has largelyfocused on downstream modules and fusion strategies rather than revis-iting the backbone itself. To help close this gap in cross-domain perfor-mance and improve the DCT backbone design, we propose DiffNet, arelatively simple yet effective RGB–DCT early-fusion architecture drivenby two key design choices. First, to ensure that the decoder aggregatesmulti-scale inconsistency evidence rather than operating on raw, content-heavy activations, we apply a lightweight multi-level discrepancy trans-formation at the output of each backbone stage, replacing features withmagnitude-only responses to learned zero-sum filters. Second, we designan efficient DCT-domain backbone that relies on a lightweight frequency-index-aware DCT–quantization joint embedding. Our approach achievesstate-of-the-art performance on cross-domain and human-made docu-ment tampering localization, outperforming prior methods by around30%, with up to 7× higher throughput than the previous best model.