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Commit a5389d2c authored by Taburet Théo's avatar Taburet Théo
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%% Cell type:code id: tags:
``` python
#Import depencies
from NS.sampling import BIL as Sampler
import os
import rawpy
import matplotlib.pyplot as plt
```
%% Cell type:markdown id: tags:
# Embedding
This notebook is a toy example which perform a cover source switching from ISO100 to ISO200 using the rights *(a, b)* for the database. The sampling can be done using different methods : MVG, Cholesky and Rejection-Sampling.
%% Cell type:code id: tags:
``` python
cwd = os.getcwd()
cover_path = cwd+'/ISO100/EYED5607.DNG'
cover_name = os.path.basename(cover_path)
raw_cover = rawpy.imread(cover_path)
```
%% Cell type:markdown id: tags:
## 1 Sampling of undecimated DCT coefficients
Available sampling strategies :
* Using MVG/Cholesky over 4 lattices
* Using MVG/Cholesky to preserve intra correlations only
* Using MVG/Cholesky to sample under independance hypothesis of DCT coefficients
%% Cell type:markdown id: tags:
### 1.1.a Using MVG/Cholesky over 4 lattices
The two lines were used to obtain the result of the **Table I** and row **'J-Cov-NS'**.
| PE (%)/<br>JPEG QF | J-Cov-NS |
| :--- | :----: |
| 100 | 42.9 |
| 95 | 41.2 |
| 85 | 41.2 |
| 75 | 41.6 |
%% Cell type:code id: tags:
``` python
#sampler = Sampler.MVG(cover = raw_cover, seed = 1234) #MVG
#sampler = Sampler.Cholesky(cover = raw_cover, seed = 1234) #Cholesky
```
%% Cell type:markdown id: tags:
### 1.1.b Using MVG/Cholesky to preserve intra correlations only
To obtain the result of the **Table I** and row **'Intra-block correlations only'**.
| PE (%)/<br>JPEG QF |Intra-block<br>correlations only |
| :--- | :----: |
| 100 | 0.0 |
| 95 | 0.2 |
| 85 | 15.8 |
| 75 | 25.2 |
%% Cell type:code id: tags:
``` python
#sampler = Sampler.MVG(cover = raw_cover, seed = 1234, strategy = "Intra-only") #MVG
sampler = Sampler.Cholesky(cover = raw_cover, seed = 1234, strategy = "Intra-only") #Cholesky
```
%% Cell type:markdown id: tags:
### 1.1.c Using MVG/Cholesky to sample under independance hypothesis of DCT coefficients
Used to confirm the results obtained by Tomáš Denemark, Patrick Bas, and Jessica Fridrich in the paper called *'Natural Steganography in JPEG Compressed Images'* published in *'Electronic Imaging 2018'* and shown in the article at **Table I** and row **'Independent embedding'**.
| PE (%)/<br>JPEG QF |Independent<br>embedding |
| :--- | :----: |
| 100 | 0.0 |
| 95 | 0.5 |
| 85 | 10.8 |
| 75 | 27.0 |
%% Cell type:code id: tags:
``` python
#sampler = Sampler.MVG(cover = raw_cover, seed = 1234, strategy = "Independant") #MVG
#sampler = Sampler.Cholesky(cover = raw_cover, seed = 1234, strategy = "Independant") #Cholesky
```
%% Cell type:markdown id: tags:
### 1.2 Quantization and full-frame cover's splitting
This code generate a quantized full-frame version of the undecimated DCT version of the stego and split into *512x512* quantized arrays.
%% Cell type:code id: tags:
``` python
stego, crops = sampler.computeStego().getStego(Qf = 100, crop_size = (512, 512))
```
%% Cell type:markdown id: tags:
## 2 Sampling of quantized DCT coefficients
%% Cell type:markdown id: tags:
### 2.a Pseudo-embedding
Available sampling strategies :
* Pseudo-embedding
Details at section **II.C.1** and **Figure 1**, and performance of this embedding shown in the article at **Table I** and row **'Pseudo embedding'**.
| PE (%)/<br>JPEG QF |Pseudo<br>embedding |
| :--- | :----: |
| 100 | 40.2 |
| 95 | 40.9 |
| 85 | 41.9 |
| 75 | 41.3 |
%% Cell type:code id: tags:
``` python
#Pseudo-embedding
sampler = Sampler.Sampler(cover = raw_cover, seed = 1234, strategy = "Pseudo") #Pseudo-embedding
#sampler = Sampler.Sampler(cover = raw_cover, seed = 1234, strategy = "Pseudo") #Pseudo-embedding
sampler = Sampler.Sampler(cover = raw_cover, seed = 1234, strategy = "Pseudo-spatial") #Pseudo-embedding spatial
```
%% Cell type:markdown id: tags:
### 2.b Rejection sampling
Available sampling strategies :
* Using rejection sampling over 4 lattices
* Using rejection sampling to preserve intra correlations only
* Using rejection sampling to sample under independance hypothesis of DCT coefficients
%% Cell type:code id: tags:
``` python
#RJ over 4-Lattices
#sampler = Sampler.RJ(cover = raw_cover, Qf = 100, K = 1, seed = 1234) #RJ - 4 lattices
```
%% Cell type:code id: tags:
``` python
#RJ : intra-only
#sampler = Sampler.RJ(cover = raw_cover, Qf = 100, K = 1, seed = 1234, strategy = "Intra-only") #RJ - intra
```
%% Cell type:code id: tags:
``` python
#RJ : independant
#sampler = Sampler.RJ(cover = raw_cover, Qf = 100, K = 1, seed = 1234, strategy = "Independant") #RJ - independant
```
%% Cell type:markdown id: tags:
## Quantize and crop :
%% Cell type:code id: tags:
``` python
stego, crops = sampler.computeStego().getStego(crop_size = (512, 512))
```
%% Output
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[ -73, -101, -95, ..., -49, -36, -19],
[ -50, -70, -66, ..., -35, -26, -13],
[ -25, -36, -33, ..., -18, -13, -7]], dtype=int32), array([[-1084, -79, -72, ..., -30, -22, -12],
[ -79, -106, -100, ..., -43, -30, -16],
[ -75, -101, -96, ..., -40, -28, -15],
...,
[ -62, -85, -79, ..., -34, -24, -13],
[ -43, -58, -54, ..., -23, -16, -7],
[ -22, -29, -27, ..., -12, -8, -3]], dtype=int32), array([[-1065, -54, -50, ..., -86, -59, -30],
[ -55, -75, -70, ..., -120, -83, -42],
[ -51, -70, -67, ..., -113, -78, -40],
...,
[ -41, -60, -55, ..., -67, -47, -24],
[ -27, -38, -36, ..., -47, -32, -17],
[ -14, -19, -19, ..., -24, -17, -9]], dtype=int32), array([[-1135, -152, -143, ..., -86, -60, -30],
[ -152, -211, -198, ..., -119, -82, -41],
[ -143, -198, -187, ..., -112, -77, -39],
...,
[ -86, -120, -113, ..., -68, -47, -24],
[ -59, -82, -78, ..., -47, -32, -16],
[ -30, -42, -40, ..., -24, -16, -8]], dtype=int32), array([[-1135, -152, -143, ..., -86, -60, -30],
[ -151, -210, -198, ..., -120, -83, -42],
[ -142, -198, -186, ..., -113, -78, -39],
...,
[ -87, -120, -113, ..., -68, -47, -24],
[ -60, -83, -78, ..., -47, -32, -16],
[ -30, -42, -40, ..., -24, -16, -8]], dtype=int32), array([[-1136, -152, -143, ..., -87, -59, -30],
[ -152, -211, -199, ..., -120, -82, -42],
[ -143, -199, -188, ..., -112, -77, -40],
...,
[ -86, -119, -112, ..., -68, -47, -24],
[ -59, -82, -77, ..., -47, -32, -16],
[ -30, -42, -40, ..., -25, -17, -9]], dtype=int32), array([[-1135, -152, -144, ..., -86, -59, -30],
[ -152, -211, -199, ..., -120, -82, -42],
[ -144, -199, -187, ..., -113, -78, -40],
...,
[ -87, -120, -113, ..., -68, -47, -24],
[ -60, -83, -78, ..., -47, -33, -17],
[ -31, -42, -40, ..., -24, -17, -8]], dtype=int32), array([[-1136, -153, -144, ..., -87, -60, -30],
[ -153, -212, -199, ..., -120, -83, -42],
[ -144, -199, -187, ..., -113, -78, -40],
...,
[ -87, -121, -113, ..., -69, -47, -24],
[ -60, -83, -78, ..., -47, -33, -17],
[ -31, -43, -40, ..., -24, -17, -9]], dtype=int32), array([[-1136, -153, -144, ..., -87, -60, -30],
[ -153, -211, -199, ..., -121, -83, -43],
[ -144, -199, -187, ..., -114, -79, -40],
...,
[ -88, -122, -114, ..., -68, -46, -23],
[ -60, -84, -79, ..., -46, -31, -16],
[ -31, -43, -40, ..., -24, -16, -8]], dtype=int32)]
%% Cell type:code id: tags:
``` python
```
......
NS/sampling/BIL/__init__.py
+ 16
− 8
View file @ a5389d2c
......@@ -48,6 +48,8 @@ class Sampler():
self.Embedding_independant()
elif(self.strategy == "Pseudo"):
self.Embedding_pseudo()
elif(self.strategy == "Pseudo-spatial"):
self.Embedding_pseudo_spatial()
return self
......@@ -237,6 +239,12 @@ class Sampler():
return self.stego
def getStego(self, Qf = 100, crop_size = (0, 0)):
if(self.strategy == "Pseudo-spatial"):
self.stego_quantized = (self.stego/256).astype(dtype=np.uint8)
if(crop_size != (0, 0) and len(list(crop_size)) == 2):
s_i, s_j = self.stego.shape[0]//crop_size[0], self.stego.shape[1]//crop_size[1]
return self.stego_quantized, [ self.stego_quantized[crop_size[0]*i:crop_size[0]*(i+1), crop_size[1]*j:crop_size[1]*(j+1)] for i, j in itertools.product(range(s_i), range(s_j)) ]
else :
Q_Y = np.load('Q_Y.npy')[Qf-1]
self.stego_quantized = compute_jpeg_from_dct(compute_dct_domain(self.stego), Q_Y)[8:-16,16:-8]
......@@ -244,7 +252,7 @@ class Sampler():
s_i, s_j = self.stego.shape[0]//crop_size[0], self.stego.shape[1]//crop_size[1]
return self.stego_quantized, [ self.stego_quantized[crop_size[0]*i:crop_size[0]*(i+1), crop_size[1]*j:crop_size[1]*(j+1)] for i, j in itertools.product(range(s_i), range(s_j)) ]
else:
return self.stego_quantized
return self.stego_quantized, self.stego_quantized
class MVG(Sampler):
def __init__(self, cover, seed, strategy = "4-Lattices"):
......
NS/sampling/BIL/__pycache__/__init__.cpython-37.pyc
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− 0
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NS/tools/__pycache__/__init__.cpython-37.pyc
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