Adaptive MPEG-2 Video Data Hiding Scheme

Anindya Sarkar, Upmanyu Madhow, Shivkumar Chandrasekaran, B. S. Manjunath

Presented by:

Anindya SarkarVision Research Lab,

Department of Electrical & Computer Engg,University of California, Santa Barbara

Januray 31, 2007

April 18, 2023

Organization of the Talk

• Problem at hand – high volume video data hiding

• Relevant still-image based past work - Selectively Embedding in Coefficients (SEC) based scheme for data hiding in images, with repeat-accumulate (RA) coding based error redundancy added to tackle channel errors and erasures

• Extending scheme to videos – incorporating adaptiveness in the scheme

• Experiments and Results

• Future work and related issues

April 18, 2023

Video Data Hiding1) A video can be treated as a sequence of frames – thus, by hiding per

frame, we can embed and transmit a large amount of data – motivated by our work on high volume data hiding for still images

2) But what are the trade-offs? Image suffers spatial compression; e.g. a MPEG-2 video is

compressed both spatially (in transform domain) and temporally using motion compensation – thus, coefficient perturbations are more difficult to predict for videos

3) For still-image based hiding in videos, robustness is more difficult to ensure for videos – the challenge is to design a hiding scheme to embed as much data as possible and being able to retrieve it

April 18, 2023

DataEmbedded

UsingSEC

Scheme (redundancy

addedusing RA –q

code)

MPEG-2Encoder

Channel consistsof one/more

stages of MPEG-2 decodingand encoding (attack)

MPEG-2Decoder

ExtractionOf

EmbeddedData usingRA decoder

Video V1

decoded

Using MPEG -2decoder

Sequence offrames: X1X2…XN

Modified frames:X’1X’2…X’N

containingembedded dataMessage m

Video V2

Video V3

DecodedFrame sequenceX’’1X’’2…X’’N

Message m’

April 18, 2023

Data Hiding Scheme for Images

Image

2D DCT

Divide by JPEG

quantization matrix

Choose coefficients

to hide

Hide using choice of scalar quantizer

•Quantize to odd values to hide ‘1’

•Quantize to even values to hide ‘0’

Scaling and Inverse DCT

DCT “Coefficients”

Image Adaptive Criterion

Divide image into 8x8 non overlapping

blocks

Basic Data Hiding Scheme proposed in VRL : courtesy, Kaushal M. Solanki

April 18, 2023

Error Resilience -Turbo Codes• Channel noise may cause

– Insertion (wrongly finding hidden data)– Deletion (wrongly missing hidden data)

• These may lead to de-synchronization & decoding failure (we can survive some decoding errors but de-synchronization can be the KILLER – so we use erasures to allow for synchronization)

• Solution – use all selected coefficients in a frame to construct a long codeword, with proper redundancy thrown in– Use Repeat accumulate (RA) codes for their near

capacity performance for erasure channels

April 18, 2023

Video Data Hiding: Key Points

1) Hiding done per frame in uncompressed domain by scalar quantization index modulation on a selected set of mid-band DCT terms

2) Embedding rate is varied according to the type of frame and the MPEG-2 determined quantization parameter, that determines the bit-rate allocation per frame

3) Estimating the capacity of MPEG-2 compression channel – using the estimate to determine the code redundancy factor needed to reliably decode embedded data

April 18, 2023

Adaptive Hiding• Hide different amounts in I,P and B frames: why? Varying

distortions per frames – so varying levels of robustness

• I-frames- intra-coded macroblocks -> less distortion -> hide more

• B-frames- inter-coded macroblocks -> bidirectional prediction -> more distortion -> hide less

• Greater spatial activity -> smaller quantization parameter -> more variance per macroblock -> higher avg. value of DCT terms -> more midband DCT terms whose magnitude exceeds T -> more terms available for hiding by SEC scheme -> hide more

April 18, 2023

Parameters to vary in hiding scheme:

• Number (n) of AC DCT coefficients, chosen by zigzag scan, to embed data per 8x8 block, per frame

• The RA code redundancy factor (q)

• The quantization interval used in QIM scheme (Δ)

• Design quality factor (QF) used for quantization of DCT coefficients

Thus, effective data bits/8x8 block = n/q

April 18, 2023

X(databits)

Y(obtained

afterapplying

RA-q codingon X)

Y is used tomodify

quantizedDCT

coefficientsto embed

Z

Z(data

embeddedin modified

DCTcoefficients)

MPEG-2Channel(attack)

Z’(data

extractedfrom

DCT coefficientsobtained

from decodedframes)

Set initialLLR values

using Z’in RA-q

decoder;decode to get

X’

X isbinarydatasequenceof lengthN

Y isbinarycodedsequenceobtainedfrom X;of lengthN*q

The embeddeddata Zconsistsof 0,1 and e;of lengthN*q

The extracteddata Z’consistsof 0,1 and e;of lengthN*q

Decoded output binary sequence X’ of length N

2 by 3 matrixmapping 0,1 in Yto 0,1,e in Z

3 by 3 matrixmapping 0,1,e in Zto 0,1,e in Z’

MAPPING FROM Y TO Z’ GIVES CHANNEL CAPACITY

April 18, 2023

Binary sequence Y gets mapped to {0,1,e} in Z’

April 18, 2023

CHANNEL CAPACITY COMPUTATION

For capacity computation, we need to maximizethe mutual information between the input (Y ) and output

(Z0) terms in the probabilistic part of the channel.

I (Y;Z0) = maxp(y)

X

y2f 0;1g; z02f 0;1;eg

p(y;z0) log½

p(yjz0)p(y)

¾

Once we compute the capacity value (C) for a frame,the optimum e®ective redundancy factor (qef f ) should be d1

Ce.

A fter ¯xing n,¢ and QF (hiding parameters)and the parameters used in M P EG-2 compression,

we can compute C which in turn helps in estimating q.

April 18, 2023

Adaptive Hiding Setup• Here, by channel – we mean a hiding setup where a certain set of hiding

parameters are used

• We divide frames into 3 channels – Each I/P/B channel is sub-divided further into 3-5 channels

• The MPEG-2 avg. quantization parameter is called “mquant” (it takes values in range [1-31]) :

higher “mquant” -> coarser quantization (less bits allocated for compression of that frame )

• For frames with higher mquant (less spatial activity), we use lower n/q (hide less data) and lower QF (coarser quantization for increased robustness) and vice versa

April 18, 2023

Experiment setup details

• We perform experiment on 1.5Mbps videos having a GOP length of 12 frames (I-P frame distance=3 frames)

• Noise introduced by MPEG-2 attack – – 4 & 1.5 Mbps in one case– 8 & 1.5 Mbps in the other– GOP size was varied (6, 12, 15, 18, 24)

• We vary number of channels (1, 3, 9, 15)– 1 - non-adaptive– 3 - I/P/B– 9/15 - each I/P/B channel is subdivided into 3/5 channels

demos

April 18, 2023

In theadaptive rate control schemeof MPEG-2,themquant parameter is clipped to the range [1..31].

Let (3i ¡ 2)th; (3i ¡ 1)th and (3i)th channels,for 1 · i · 5, correspond to I, P and B frames, respectively.

n1 = 6; q1 = 15; QF1 = 50;n2 = 4; q2 = 15; QF2 = 40;n3 = 2; q3 = 15; QF3 = 30;

9=

;11 · mquant · 15

ni = ni ¡ 3¡ 1; qi = qi ¡ 3+4; QF i = QF i ¡ 3¡ 5; 4 · i · 6; 16 · mquant · 22ni = ni ¡ 6+1; qi = qi ¡ 6¡ 2; QF i = QF i ¡ 6+5; 7 · i · 9; 6 · mquant · 10ni = ni ¡ 9 ¡ 1; qi = qi ¡ 9 +7; QF i = QF i ¡ 9 ¡ 5; 10 · i · 12; mquant > 22ni = ni ¡ 12+2; qi = qi ¡ 12 ¡ 2; QF i = QF i ¡ 12+5; 13· i · 15; mquant · 5

Parameter allocation for 15 channel case, which was empirically decided upon through

experimentation

April 18, 2023

Variable Parameters – Decoding Issues

• For a multi-channel approach, receiver has to be told beforehand about the 9/15 different parameter sets that can be used

• If hiding parameters match at the encoder and decoder sides (& compression noise is small enough), RA decoding converges

• So, we vary the parameters (over all 9/15 sets) and choose that set for which RA decoding does converge

April 18, 2023

GOPsize

No. of Channel

s

Bits/frame

4 Mbps & 1.5 Mbps 8 Mbps & 1.5 Mbps

FER(frame

Error rate)

Flicker PSNR(dB)

FER(frame

Error rate)

Flicker PSNR(dB)

6

1 240.0 .140 21.54 36.40 .134 21.08 36.62

3 227.2 .091 22.89 36.38 .085 22.49 36.60

9 277.2 .103 22.56 36.34 .074 22.16 36.56

15 294.2 .082 22.41 36.26 .074 21.99 36.49

15

1 240.0 .068 20.73 37.05 .054 20.31 37.30

3 227.2 .037 22.21 37.03 .025 21.78 37.28

9 277.2 .014 21.81 36.99 .017 21.35 37.25

15 294.2 .011 21.68 36.92 .011 21.20 37.18

24

1 240.0 .065 20.51 37.16 .051 20.07 37.40

3 227.2 .017 22.02 37.13 .022 21.59 37.38

9 277.2 .017 21.70 37.09 .008 21.20 37.34

15 294.2 .008 21.48 37.02 .005 21.00 37.28

April 18, 2023

Summary of the Video Work

• Using higher number of channels, we can embed about 20% more data while frame error rate is also reduced (by a factor of 2-8 depending on the GOP size)

• We embed about 300 bits/frame: 25 frames/sec -> 7500 bps of embedded data

• PSNR difference between single & 15-channel schemes ~ 0.2 dB

• Perceptual quality improves as GOP size increases

April 18, 2023

Scope for Future Work

• We aim to design a scheme to reduce the temporal flicker, almost always present in frame-by-frame hiding schemes for videos

• Problem – Encoding of a B or P frame depends on other frames – temporal relationship (motion vectors) is not maintained as we modify the reference frames by hiding.

• In hiding scheme, we should include the temporal information and knowledge about the distortion of the reference frames, used for frame prediction

April 18, 2023

Thanks for your patience.

Questions?

April 18, 2023

Scalar version of QIM scheme – used for data hiding