python 3.7
transformers 4.12
- TS-CSW: text steganalysis and hidden capacity estimation based on convolutional sliding windows (TS-CSW)
- TS-RNN: Text Steganalysis Based on Recurrent Neural Networks (TS-BiRNN)
- Linguistic Steganalysis via Densely Connected LSTM with Feature Pyramid (BiLISTM-Dense)
- A Fast and Efficient Text Steganalysis Method (TS-FCN)
- A Hybrid R-BILSTM-C Neural Network Based Text Steganalysis(R-BiLSTM-C)
- Linguistic Steganalysis With Graph Neural Networks (GNN) (waiting......)
- SeSy: Linguistic Steganalysis Framework Integrating Semantic and Syntactic Features (Sesy)
- High-Performance Linguistic Steganalysis, Capacity Estimation and Steganographic Positioning (BERT-LSTM-ATT)
- 使用命令行：
python main.py --config_path your_config_file_path - 例如：
python main.py --config_path ./configs/test.json - 作为参考，./configs/test.json 里包含多种方法的超参数设置（存在冗余，根据自己需求删改），其中"use_plm"用来控制是否需要预训练语言模型，"model"表示使用哪种方法，根据需求修改"Training_with_Processor"或者"Training"参数，推荐前者即使用Processor进行预处理。默认情况下，我们只设置了num_train_epochs=1, 在很多数据集下，是不能很好收敛的。
- 例如，如果想使用TS-FCN方法，一个最简单的方法就是将test.json里的"model"改为"fcn","use_plm"改为false, 然后设置合适的学习率、epoch、batchsize 等等(在Training_with_Processor中)
- Use Command:
python main.py --config_path your_config_file_path - For example:
python main.py --config_path ./configs/test.json - As a reference, ./configs/test.json contains the hyper-parameters settings of various methods (redundant, delete and modify up to your own setting). Among them, "use_plm" is used to control whether the pretrained language model (PLM) is used to embed words. "model" indicates which method to be use. Modify the "training_with_processor" or "training" parameters according to your needs. It is recommended that the former, namely using processor for pre-processing your data. By default, we only set **num_ Train_ Epochs = 1, where models can not converge well in many datasets **.
- For example, if you want to try the TS-FCN method, a very easy way is to set "model" as "fcn", set "use_plm" as false, and then set the appropriate learning rate, epoch, batchsize, etc. (in Training_with_Processor), based on "test.json".
- 之前的sesy config不是论文中使用的config. 已修复(2023.03)。
- 如果使用此代码进行不同方法的对比时，请仔细确认数据集的拆分方式是否相同。
- 可以在config.json 的Dataset中设置自己的“split_ratio”，例如设置为0.8, 则会产生0.2的test.csv”, 0.2的“val.csv”和0.6的“train.csv”
- 或者也可以将“stego_txt”和“cover_txt”设置为None，设置“resplit"为False，并将数据集放在“csv_dir”所指示的文件路径中。放置的数据集应根据对比实验的需要，事先自行拆分为“train.csv”、“val.csv”和“test.csv”。
- Previous sesy config is not the configs used in paper SESY. We have fixed it.
- When using this repo for camparation, please carefully confirm whether the spliting of data set is the same.
- You can reset the "split_ratio" in Dataset config. For example, if set it to 0.8, you will get 0.2 "test.csv", 0.2 "val.csv", and 0.6 "train. csv".
- Or, you can set stego_txt and cover_txt as None, set "resplit" as False, and put your dataset in path "csv_dir". The dataset should beforehand be split into "train.csv", "val.csv" and "test.csv" by yourself.
基础 R-BiLSTM-C BiLISTM-Dense MS-TL 模型的实现参考自CAU-Tstega/Text-steganalysis
implements of R-BiLSTM-C BiLISTM-Dense MS-TL refer to CAU-Tstega/Text-steganalysis