Name	File	SW	Proposed
MODE loss	MODE-loss	Pytorch	[1]
Robust Antecedent UERD	Antecedent_UERD.zip	Python	[2]
Adobe detector	adobe-detector	Python	[3]
Adobe PRNU removal	prnu-python	Python	[4]
Robust UERD	Robust_UERD.py	Python	[5]
JEEP	JEEP.py	Python	[6]
SVP	SVP.py	Python	[7]
JIN-SRNet	JIN_SRNet.zip	PyTorch	[8]
PQ-UNIWARD	PQ-UNIWARD_matlab.zip	Matlab	[9]
PL Steganalysis	pytorch-lightning-steganalysis	Pytorch	-
JPEG Tools	JPEG_utils.py	Python	-
Embedding Simulator	embedding_simulator.py	Python	-
ixlnx2	ixlnx2.npy	NumPy	-

References

1. MODE: Loss Function for Deep Steganalysis at Low False Positive Rate
   Butora, J., & Bas, P.
   33rd European Signal Processing Conference
   paper
2. Errorless Robust JPEG Steganography for Pixel Images
   Butora, J., Bas, P., & Levecque, E.
   IEEE WIFS 2024
   paper
3. Detection of the Adobe Pattern
   Butora, J., & Bas, P.
   32nd European Signal Processing Conference
   paper
4. The Adobe Hidden Feature and its Impact on Sensor Attribution
   Butora, J., & Bas, P.
   12th IH&MMSec. Workshop 2024
   paper
5. Errorless Robust JPEG Steganography using Outputs of JPEG Coders
   Butora, J., Puteaux, P., & Bas, P.
   IEEE Transactions on Dependable and Secure Computing
   paper
6. Side-Informed Steganography for JPEG Images by Modeling Decompressed Images
   Butora, J., & Bas, P.
   IEEE Transactions on Information Forensics and Security
   paper
7. Fighting the Reverse JPEG Compatibility Attack: Pick your Side
   Butora, J., & Bas, P.
   10th IH&MMSec. Workshop 2022
   paper
8. How to Pretrain for Steganalysis
   Butora, J., Yousfi, Y., & Fridrich, J.
   9th IH&MMSec. Workshop 2021
   paper
9. Revisiting Perturbed Quantization
   Butora, J., & Fridrich, J.
   9th IH&MMSec. Workshop 2021
   paper