Adaptive MPEG-2 Video Data Hiding Scheme Anindya Sarkar, Upmanyu Madhow, Shivkumar Chandrasekaran, B. S. Manjunath Presented by: Anindya Sarkar Vision Research Lab, Department of Electrical & Computer Engg, University of California, Santa Barbara Januray 31, 2007

June 11, 2015 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

June 11, 2015 Video Data Hiding 1)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

June 11, 2015 Data Embedded Using SEC Scheme (redundancy added using RA –q code) MPEG-2 Encoder Channel consists of one/more stages of MPEG-2 decoding and encoding (attack) MPEG-2 Decoder Extraction Of Embedded Data using RA decoder Video V 1 decoded Using MPEG -2 decoder Sequence of frames: X 1 X 2 …X N Modified frames: X’ 1 X’ 2 …X’ N containing embedded data Message m Video V 2 Video V 3 Decoded Frame sequence X’’ 1 X’’ 2 …X’’ N Message m’

June 11, 2015 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

June 11, 2015 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

June 11, 2015 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

June 11, 2015 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

June 11, 2015 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

June 11, 2015 X (data bits) Y (obtained after applying RA-q coding on X) Y is used to modify quantized DCT coefficients to embed Z (data embedded in modified DCT coefficients) MPEG-2 Channel (attack) Z’ (data extracted from DCT coefficients obtained from decoded frames) Set initial LLR values using Z’ in RA-q decoder; decode to get X’ X is binary data sequence of length N Y is binary coded sequence obtained from X; of length N*q The embedded data Z consists of 0,1 and e; of length N*q The extracted data Z’ consists of 0,1 and e; of length N*q Decoded output binary sequence X’ of length N 2 by 3 matrix mapping 0,1 in Y to 0,1,e in Z 3 by 3 matrix mapping 0,1,e in Z to 0,1,e in Z’ MAPPING FROM Y TO Z’ GIVES CHANNEL CAPACITY

June 11, 2015 Binary sequence Y gets mapped to {0,1,e} in Z’

June 11, 2015 CHANNEL CAPACITY COMPUTATION

June 11, 2015 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

June 11, 2015 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

June 11, 2015 Parameter allocation for 15 channel case, which was empirically decided upon through experimentation

June 11, 2015 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

June 11, 2015 GOP size No. of Channel s Bits/ frame 4 Mbps & 1.5 Mbps8 Mbps & 1.5 Mbps FER (frame Error rate) FlickerPSNR (dB) FER (frame Error rate) FlickerPSNR (dB)

June 11, 2015 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

June 11, 2015 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

June 11, 2015 Thanks for your patience. Questions?

June 11, 2015 Scalar version of QIM scheme – used for data hiding