1. High Capacity Data Embedding in JPEG Bit Streams Using Visual Models
Perry Nakar
Ofer Hermoni
Supervised By Dr. Ofer Hadar

2. Preface
In order to understand this presentation one needs a basic knowledge of:
Mathematics: the concept of modulo.
Multimedia: JPEG image compression The PSNR value.

3. Introduction This is the multimedia era!!!
Digital images are widespread.
Digital images need to be protected; one way is by adding an invisible signal known as a ‘digital watermark’.
Many applications use the JPEG compression standard, so adding the digital watermark to the compressed image is essential.
So far the known methods enabled only a small amount of data embedding, or reduced the image quality too much….

4. Method I - Main Idea modulo of summed DCT coefficients
Our method uses modulo of summed DCT coefficients, after quantization and rounding, to store additional information by forcing modulo to a desired state by adjusting some of the DCT coefficients.
Example N = 3 (no. of bits to embed per block 23 = 8 options)
The data to embed is 5 = 101 2 3
Changing 2 coefficients + 2 3 1 1 2
Changing 6 coefficients -
sum = 3 mod 8
sum = 5 mod 8

5. Problem Which DCT coefficients should be modified?

6. The Embedding Scheme Pre- processing and embedding JPEG Bit stream
Original
Image
Quantization
Table
Division to
blocks
DCT
Entropy
coding
Quantizer
Transformed Image
Modified
DCT
coefficients
after
rounding
DCT
coefficients
before
rounding
Pre-
processing
and
embedding
Bi,j
Source
and
Channel
coding
Watermark

7. Criterions I. Half Way Close
Dq(z) – the DCT coefficient after Q before rounding
Dr(z) – the DCT coefficient after Q after rounding
Example 3 2 1
2.8
2.6
1.1
0.4
Dq(z)=2.8
Dr(z)=3
Erup=0.7
Erdn=0.3
Dq(z)=2.6
Dr(z)=3
Erup=0.9
Erdn=0.1
Dq(z)=1.1
Dr(z)=1
Erup=0.4
Erdn=0.6
Dq(z)=0.4
Dr(z)=0
Erup=0.1
Erdn=0.9

8. Criterions II. HVS based - Magnitude
D(Z) – the absolute of the
original DCT coefficient

9. Criterions III. HVS based – Coefficient Position1

10. Criterions Total Error

11. Criterions - summary
Eup(n) = {Eup(1) … Eup(Nmax_up)}  Eup(n) < Eup(n+1)
Edn(m) = {Edn(1) … Edn(Mmax_dn)}  Edn(m) < Edn(m+1)

12. = Experiment Embedding Reconstruction Original Image Embedded Image…
Reconstruction
Watermark = …
Reconstructed data

13. number of embedded bits is 6 per block, total of 24,576 per picture.
Results I
number of embedded bits is 6 per block, total of 24,576 per picture.
PSNR of compressed host with hidden data (dB)
6 bits per block – total of 24,576
4 bits per block – total of 16,384
PSNR of compressed host without hidden data (dB)
Quality
Factor q
Lena
512x512 5 10 20 40 60 80

14. Results I – cont. Original Image JPEG no embedding q=10 JPEG embedded
Results I – cont.
Original Image
JPEG no embedding
q=10
JPEG embedded
M=6 ; q=10
Total of 24,576 bits
PSNR = dB
PSNR = dB

15. The Adaptive method
The quantity of bits to be embedded in each block is based on the JND of the block itself.
According to threshold levels based on JND, an adequate number of bits are embedded in each block, using the same parameters as method I.
Reconstruction is done using the same threshold levels.
The main problem – in the reconstruction procedure the threshold may be changed, therefore the quantity of bits which had been implanted may not be recollected.
Solution – recalculate threshold levels after embedding and if changed embed the new number of bits.

16. Our adaptive method vs. the “gold-standard” J-Mark method.
Results II
Lenna 512x512
Our adaptive method vs. the “gold-standard” J-Mark method.

17. Our adaptive method vs. the “gold-standard” J-Mark method.
Results II – cont.
Lenna 512x512
Our adaptive method vs. the “gold-standard” J-Mark method.

18. Conclusions
Our first method embeds a specifiable number of bits into an image.
The embedded data is fully recoverable without using the original image.
The compression rate is not affected.
The image quality is hardly damaged.
The standard JPEG coder/decoder is used.
The algorithm is fast and can be used to embed data into M-JPEG movies.
Etc. Etc…

19. Questions