  Document tamper detection has always been an important aspect of tamper
detection. Before the advent of deep learning, document tamper detection was
difficult. We have made some explorations in the field of text tamper detection
based on deep learning. Our Ps tamper detection method includes three steps:
feature assistance, audit point positioning, and tamper recognition. It
involves hierarchical filtering and graded output (tampered/suspected
tampered/untampered). By combining artificial tamper data features, we simulate
and augment data samples in various scenarios (cropping with noise
addition/replacement, single character/space replacement, smearing/splicing,
brightness/contrast adjustment, etc.). The auxiliary features include
exif/binary stream keyword retrieval/noise, which are used for branch detection
based on the results. Audit point positioning uses detection frameworks and
controls thresholds for high and low density detection. Tamper recognition
employs a dual-path dual-stream recognition network, with RGB and ELA stream
feature extraction. After dimensionality reduction through self-correlation
percentile pooling, the fused output is processed through vlad, yielding an
accuracy of 0.804, recall of 0.659, and precision of 0.913.
