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AMLAN CHAKRABORTY

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Steganography and Digital Watermarking -- Applications,

Attacks and Countermeasures

IntroductionSteganography is the science of hiding information in data. Normally steganography is doneintelligently such that it is difficult for an adversary to detect the existence of a hidden message in

the otherwise innocuous data. The piece of data that has the message embedded in it is visible to

the world in the clear and appears as harmless and normal. This is in stark contrast with

cryptography where the message is scrambled to make it extremely difficult or impossible for an

adversary to put together. A message in ciphertext arouses some sort of suspicion whereas

invisible message embedded in clear text does not. This is the advantage of steganography.

Generally, a steganographic message will appear to be something else: a picture, an audio file, a

video file or a message in clear text - thecovertext. Historically, messages were written using

hidden invisible ink between the visible lines of innocuous documents, or even written ontoclothing. Other techniques used were writing messages in Morse code in knitting yarn, or

marking particular words or letters in the message, using invisible ink or pin prick that form the

secret message. During WWII Germans used the microdot technology, where an image the size of

a period had the clarity of typewritten pages. In this case the period was the covertext and the

image is the message. Though smart hiding and innocuous hiding techniques are used to hide the

stegotext, the algorithm itself is secure and only known to the communicating parties and not to

the world. This is in slight contrast to classical cryptography where the algorithm is well known

and only the key(s) are secret. Though data is not encrypted in steganography, authenticity of a

message is normally established by using a MAC or a signature.

Steganography can be used to code messages in any transport layer – an image (GIF/BMP/JPEG),

a MP3 file, a communications protocol like UDP etc. Steganogrpahic information can also be

added to richer multimedia content like DVDs. There are normally two motivations – to send a

secret message or to establish authenticity of a piece of information – usually a multimedia file.

The later is a major application of modern steganography and known asDigital Watermarking

andFingerprinting. Watermarks establish ownership of an artifact while fingerprints or labels

help to identify intellectual property violators. They are different protocol implementation of the

same basic idea.

Information theory and human sensory perceptionSteganography is possible for the same reasons that compression is – a combination of

information theory and human perception of vision and audio. Digital signal containsredundancy which manifests itself as noise. Humans cannot detect all levels of noise; in other

words, humans often cannot tell an image or an audio clip from another with slight difference in

levels of noise. The larger the cover message is (in data content terms — number of bits) relative

to the hidden message, the easier it is to hide the latter. For example, a 24 bit bitmap image has 8

bits representing three colors – Red, green and blue at each pixel – 256 shades of each basic color.

So changing the least significant bit of any of these basic colors would make an extremely

negligible change on that pixel – and possibly less on the image. So the least significant bit can be


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easily used to store the steganographic message. So, if we change the LSB of each basic color of

three adjacent pixels, we get 9 bits -- enough space to store an ASCII character. This is calledLSB

manipulation and a very conventional and simple steganographic implementation. It can also be

noted that the actual message itself can be compressed using some compression coding

methodologies like run length coding. Historically, a lot of invisible ink steganographic messages

were encoded using Polybius squares or similar text to integer mapping schemes.

Stated somewhat more formally, the objective for making steganographic encoding difficult to

detect is to ensure that the changes to the carrier/container (the original signal) due to the

injection of the payload (the signal to covertly embed) are visually and ideally, statistically

negligible; that is to say, the changes are indistinguishable from the Gaussian noise of the carrier.

From an information theoretical point of view, this means that the channel must have more

capacity than the 'surface' signal requires (entropy), i.e., there is redundancy. For a digital image,

this may be noise from the imaging element; for digital audio, it may be noise from recording

techniques – amplitude or frequency modulation. Any system with an analog (signal)

amplification stage will also introduce thermal noise, which can be exploited as a noise cover.

Steganographic channel is acovert channelin Information theory terms since it transfers somekind of information using a method originally not intended to transfer this kind of information.

Steganography also supports both storage and timing covert channels. This report primarily

discusses storage covert channels where a covert message is communicated by manipulating a

stored object like an image. Ron Rivest’s “Chaffing and Winnowing” protocol discussed later can

be argued as an example of timing covert channel.

It is fairly obvious that more the data content of the cover message, the easier it is to hide the

message. In case of images, bitmaps are better fits that GIFs and JPEGs because GIF is 8 bits per

pixel and JPEG is a lossy compression technique. But on the flipside, bigger images will attract

more attention than smaller images as suspect stego-images. Subtlety in changes is a very

important feature and stego-images should only have subtle changes. An image with large areas

of solid colors would be a bad fit since large variances created by the embedded message would

cause drastic differences easily spotted by the human eye. The spatial frequency distribution of

the image (spatio- temporal in case of audio or video content) is also a determining factor in the

efficiency of the hiding process. As we will see later, we have techniques for both Gaussian and

LaPlacian distribution using maximum likelihood estimators for the stego-messages.

Often the embedded message is itself encrypted using a key that may or may not be known to the

adversary. Since steganography requires that communicating parties have some prior shared

information, symmetric key is a natural fit. However, public steganography with steganographic

key exchanges is also possible.

Prisoner’s problem and subliminal channelThe study of steganography in machine cryptography was first stated in the prisoner’s problem

by Simmons. Two inmates Alice and Bob are accomplices in a crime and are sent to the prison.

They need to communicate with each other but they have to use a public channel which is

monitored by the Warden of the jail. The warden will only forward the messages if they are

intelligible. The prisoners accept this condition and find a way to communicate secretly in

exchanges --- establishing asubliminal channel even though the messages themselves are not


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encrypted. The warden will also try to deceive them, so they will authenticate each other’s

messages before accepting them –authentication without secrecy.

Thethe situation is paradoxical because the warden demands access and the prisoner’s need to

authenticate each other. Authentication without secrecy channels achieve that by placing a pre

arranged condition on all messages. It is this capability that creates a subliminal channel for the

prisoners. If ‘m’ redundant bits are allowed to establish authenticity, then these redundant bitscreate a bit by bit subliminal channel which can be used to transmit extra information.

Null ciphers and “Chaffing and Winnowing”

A null cipher is a form of encryption where the plaintext is mixed with a large amount of non-

cipher material. Null ciphers are used to hide the actual ciphertext by introducing nulls to

confuse the cryptanalyst. Classical steganography can also be thought of as an extension of this

concept where the carrier / container data are actually the null ciphers – data that create

confusion and diffuse the actual payload.

Ron Rivest extended this concept to an idea of “Chaffing and Winnowing” to create

steganographic communication channels. The concept is analogous to separating (winnowing)wheat from chaff where wheat is the actual payload and chaff is the null ciphers. In a two step

process, the transmitter introduces chaff to the wheat i.e. intersperse the actual payload with

meaningless data. The receiver “winnows” the actual payload from the non-interesting data. As

with most steganographic transfers, the transmitters add a MAC to establish authenticity of the

communication to any message that is sent. MACs are calculated over the entire message and a

serial number of the message using a secret symmetric authentication key. The transmitter

attaches bogus MACs for the chaff packets instead of calculating it. This is what distinguishes the

“chaff” from the “wheat”. The receiver now doesn’t have to do anything special since the normal

protocol of a receiver is to discard packets that do not have correct MACs. Though the adversary

can see the entire communication, it cannot tell chaff from wheat as the MAC will look like a

random function. However, weak MAC functions can potentially leak information in this

protocol. It is also important to note that it is not possible to use digital signatures here since

anyone will be then able to compare the signatures and tell “chaff” from “wheat”. However,

“designated verifier signature” schemes where only signature designates can verify a signature

would work fine. The other key idea is that since the creation of “chaff” involves generation of a

bad MAC and not the knowledge of a secret key, any entity can play the role of a “chaffer”.

Digital Watermarking and FingerprintingDigital watermarking is the technique of adding identifying information to digital artifacts using

steganographic principles i.e. hiding the information cleverly so that extraction is difficult by any

adversary. Watermarks can be visible or invisible in the context of images. There are various

techniques of placing digital watermarks on images but they can conceptually be divided intotwo categories –

1. Spatial techniques. These methods are based on hiding the messages on geometric

characteristics of the image. These are highly susceptible to signal alteration algorithms.

Even simple signal manipulation like zooming, cropping, smoothing would obliterate

watermarks.

2. Frequency Domain techniques. These methods are used to hide messages along the

frequency distribution of hues, intensities, luminance etc of the images. These are


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comparatively robust to simple image manipulations but can fall prey to statistical

steganalysis.

In strict terms, visible digital watermarks are really not steganographic object – they enhance

information instead of hiding.

Fingerprintingis a slight different implementation of digital watermarks. When an artifact is sold

to an entity, information about that entity is hidden in the artifact. If illegitimate copies of the

artifact are sold, the watermark information would reveal the violator. A slight modification of

this would be using thecanary trapprotocol where unique alterations are made to each copy of

artifact sold. The illegitimate copy has a tell-a-tale that traces back to the violator.

Some digital watermarking algorithmsIt is not too difficult to formulate algorithms that can cleverly hide information in images. The key

idea to avoid detection is to hide the message in such a way that statistically it comes across like

normal distribution making pattern detection very difficult.

Masking and filtering

These are some basic techniques to create visible watermarks by altering the luminance or colors

of certain regions in the image. These can be detected very easily by simple statistical analysis but

these are fairly resistant to lossy compression and image cropping. It doesn’t hide the data in

noise but embed it in significant areas – just the reverse of LSB manipulation.

LSB Manipulation

This is the manipulation described in the Introduction that is susceptible to even slight image

modification. It is very efficient in hiding a GIF or BMP image in another but a linear analysis is

enough to figure this out. It is fairly easy to hide an image in 3 or even 4 least significant bits of

another image without causing major noticeable change. The motivation for steganography isimportant here. If the intention is to covertly pass messages, this can still work unless all artifacts

are sniffed for steganographic information. But if this is meant for digital watermarks, it is very

easy to extract and /or get rid of the info.

Spread Spectrum methods

In spread spectrum methods, the message is scattered across the image making it harder for

cropping, rotation and other basic image manipulation techniques to obliterate the watermark.

This is also somewhat resistant to statistical steganalysis because it gives it the impression of noise

in an image.Patchwork is a tool from IBM uses this technique to scatter hidden information

based on statistical distribution of luminance in the image. It iteratively selects two patches on the

image, brightens one and darkens one. It then calculates the standard deviation, S between lightand dark patches over the sample patches. To encode, it picks up two patches up in random and

then brightens one by S and darkens one by S. This process is iterated and the whole image

palette is laid in a mosaic of bright and dark patches one of which is used to hide data. This patch

information is vital to decode the hidden message later. This is clearly a frequency distribution

method. Patchwork makes the assumption that the image has a Gaussian distribution.

Texture Block coding


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In this method, pairs of areas of similar texture are found and one area is copied over the other.

Thus we have identical blocks of texture in the image. Iterating a few times, we can get two large

blocks of identical textures. These two blocks would get altered identically for all non-geometric

alterations of the image. These two blocks can then contain information about these images.

M-Sequences using linear shift registersM-sequences are based on starting vectors of a Fibonacci recursion relation which form a Galois

field of finite cardinality. Mathematically and statistically these numbers are known to have

desirable autocorrelation functions; the distribution of Galois field numbers is known to be of

normal distribution thus resembling Gaussian noise in an image. So images encoded using m-

sequences are statistically impossible to distinguish from the original as they are similar to noise

in a normal distribution. If the stego message is encoded using m-sequences, it can easily be

embedded in the image by a LSB substitution. A more secure implementation would be to use

LSB addition instead to embed the watermark. So it will require the examination of the complete

bit pattern and the current linear shift register implementation. This is more secure because to

crack this, the adversary would have to do the same computations without any apriori

knowledge.

Frequency hopping

In this method scattering of the message is done on the basis of rules that change cumulatively.

The idea is similar to DES block encryption; bits are swapped according to rules that are dictated

by the stego-key and random data from the previous round.White noise storm, an

implementation of this methodology, creates a message space of 8 channels where each channel

has a window of W bytes, where W is a random number. Each channel however carry only one bit

of the message and a lot of unused bits. The bits inside a window permutate and rotate according

to an algorithm that is regulated by the previous window’s operations and the stego-key. Finally

this encoded message is embedded in the image using LSB substitution. The idea again is to

simulate a distribution that is similar to a Gaussian distribution.

Steganalysis and Digital Watermarking AttacksSteganalysis is analogous to cryptanalysis in the context of steganography. Steganalysis is

composed of three steps:-

1. Detection of hidden message (Passive Steganalysis)

2. Extracting of hidden message (Active Steganalysis)

3.Disabling/ Destruction of hidden message.

It is important to note here that it is not necessary to extract a message to disable or destruct amessage. It is often very difficult to extract a hidden message and at times even to detect one

because they are scattered and show up as noise. The case of visible watermarks is obviously

different. But the problem lies in the fact – detection is also not important if we have a “suspicious

attitude”. We can run algorithms that are known to destruct digital watermarks in messages. On

top of that there are algorithms that instead of disabling watermarks, either overwrite

watermarks or create exact replicas – rendering the watermark useless either way. Luis Von Ahn

et al formulates and proposed “universal robustness” for steganographic information. They prove


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that “robust steganography” is as secure as the underlying crypto used to encrypt the message

that is hidden in the clear. But this just ensures extraction is hard and likens it to cryptography.

But their algorithm doesn’t prove that obliteration of the steganographic secret is not possible.

Some common types of attacks on Digital Watermarking are1.Removal attacks – Denoising, Remodulation, Lossy Compression

These attacks attempt at completely removing watermark from the data. Since a lot of

steganography algorithms try to hide data as noise, removal of noise should obliterate the

watermark. These algorithms they try to estimate the cover data using a given statistic for

the noise in it. It assumes the noise to be the watermark. Langelaar et al proposes a

sequence of filtering operations( median filtering, highpass filtering) on the image to

denoise the image that will likely get rid of the digital watermark. There are several other

watermark estimator algorithms that uses eitherMaximum Aposteriori Probability

(MAP) if we know the image statistics orMaximum likelihood (ML) Classifier

algorithms if we do not know anything about the images, to find an estimate of the

digital watermark. Voloshoynovisky proposed an algorithm where he used the MAPestimator and then remodulates the image to find the least favorable noise distribution.

This is guessed to be the watermark.

Often lossy compression of uncompressed image data like JPEG, would completely wipe

out the watermark since the raw data would be replaced by Direct Cosine Transforms of

the data. However, this is mitigated by algorithms that can hide information directly in

compressed data.

2. Geometric Attacks – Warping, transforming, jittering etc.

These attacks are the easiest to implement and often very effective. Instead of removing

the watermark, these stress on distortion of embedded data by spatial or temporal

alterations (in case of audio and video data). The result of these attacks is to scatter and

alter the way the watermark is laid out in the image. For a simple attack, if an image is

rotated by a slight angle, say 1 degree and the edges filled by the texture of the average of

adjacent pixels, there is a high likelihood that the watermark would fall out of sync with

the watermark detector. The key idea here is though the digital watermark data exists in

the artifact, it has moved in such a way that the watermark detector can no longer detect

the data.

Jitteringis another effective attack that works extremely well for audio data. An audio

signal is chunked up into “n” chunks and then either one chunk is deleted or a copy is

made and then assembled back together ending up in either (n -1) or (n +1) samples. This

introduces a jitter in the signal that is not detectable by humans. Digital watermarks

would totally get destroyed in this attack.Unzign implements a pixel jittering algorithm

that works well on spatial domain watermarks.

Another important observation is that though some algorithms survive basic geometric

attacks like rotation, shearing, resizing etc., they succumb to a combination of different

attacks.StirMarkis an implementation based on these principles that simulates an

iterative resampling process – where the image is slightly resized, sheared and rotated by

a random small amount. However, repeated iterations of StirMark degrade the image to

the point that humans can detect the difference between the original and the processed.


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3. Crypto attacks – Exhaustive key search, Collusion, Averaging, Oracle attack

These are similar to normal cryptographic attacks where the steganographic key is

searched exhaustively. Statistical averaging attacks involve taking the same data set with

different instances of watermarks and then averaging them to find the attacked data set.

A modification of the averaging algorithm is the collusion attack where smaller portionsof the data set are taken and attacked data set found using averaging algorithms. These

smaller datasets are then combined to get a new attacked data set.

4.Protocol attacks – Watermark inversion, Copy Attack.

These attacks do not aim to detect, destroy or disable the watermark, but to attack the

basic tenets of watermarking e.g. watermarks cannot be extracted from non watermarked

data.

TheWatermark inversion attackuses the feature ofovermarking that is the ability to

mark an image more than once. Bob gets an image from Alice that has her watermark.

Bob subsequently generates his own watermark and subtracts his watermark from the

image he got from Alice. Due to overmarking, Alice’s signature would still be readablefrom this image making it almost identical to the image Alice circulated. Bob can now

argue that Alice has removed his signature and added hers to generate this image. This

will establish that Bob was the actual owner of the image.

TheCopy Attack gets an estimate of the watermark using a MAP or a ML estimator. It

then processes this watermark using the least favorable noise function (mentioned in

replacement attacks) to smoothen the watermark. It then adds the watermark to a new

document. Copy attack allows anyone to identify his own document as being

watermarked by a well known entity by placing a watermark copied from a document

published by that entity on it. This is a very serious attack that Kutter et al experimentally

succeeded to accomplish.

Defenses against SteganalysisWe noticed that most steganographic algorithms pretty cleverly hide data to avoid

detection by --- blending in as Gaussian noise, embedding in significant areas, scattering

across the frequency spectrum etc. It has also been seen that it is often not easy to extract

a digital watermark. Luis von Ahn et al propose robust steganographic algorithms as well

as new advances to public key steganography etc. But this often doesn’t safeguard against

attacks to destroy or replace watermarks on images, audio files etc. We have also notices

that watermarks are particularly susceptible to attacks that are combination of more than

one attack. There have been mitigations suggested to particular types of attacks e.g. error

correction of coding theory using hamming distance (or some other distance measuring

algorithm like Euclidean algorithm ) for statistical steganalysis. But the problem is thatattacks are preceding mitigations. Barr et al from DigiMarc are suggesting the concept of

image signature to mitigate the copy attack where perpetually similar images would

produce the same signature whereas perpetually different image would produce very

different signatures. While this would successfully mitigate the copy attack, one can still

launch a geometric attack and obliterate the watermark. Since the image would be

perpetually similar the signature would be the same, and image signature would not

mitigate the attack. Additionally the problem here is that there is an additional burden on


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the watermark detector to verify the signature of the image. This is double verification

and needs additional security.

The key idea is to make the digital watermark such that destruction of the watermark

would destroy the image itself. One idea proposed by Neil Johnson is to use a gradual

mask instead of a sharp mask for the visible watermark, so that the watermark is not

visible until the luminance of the image is significantly increased. This makes it morerobust against changes of lower bits. Though extensive image processing and spatially

selective alteration of luminance based on the luminance distribution may make the

digital watermark vulnerable, the image would also be distorted enough by so much

processing.

ConclusionThe challenges in digital watermarking stem from the fact that the attacks derive from the

same phenomenon as the watermarking technology itself -- small noise insertion doesn’t

create humanly noticeable changes to an artifact. Clearly right now the watermarking

technology is not robust enough to mitigate combination of attacks. Introduction of new

authentication schemes as proposed by public key steganography would attach anotherlayer of security but does not in itself guarantee universal absolute robustness of

watermarks. I think the solution may very well lie in better statistical models based on

information theory. We can mitigate some attacks using authentication and authorization

– but pattern detection and obfuscation should be mitigated by better scattering

algorithms.


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References1.Neil Johnson and Sushil Jajodia, Exploring Steganography: Seeing the Unseen.

2.Gustavus J Simmons, The Prisoner’s Problem and the Sublimimal channel

3.Ronald L. Rivest, Chaffing and Winnowing: Confidentiality without Encryption

4.Fabien A Petitcolas, Ross J Anderson and Markus Kuhn, Information Hiding aSurvey.

5.Pierre Moulin and Joseph O’ Sullivan, Information-Theoritic Analysis of information

Hiding

6.Nicholas J Hopper, John Langford and Luis Von Ahn, Provably Secure Steganography

7. Neil Johnson and Sushil Jajodia, Steganalysis : The investigation of hidden

information

8.Bender, Gruhl, Morimoto and Lu, Techniques for data hiding.

9.Neil F Johnson, An Introduction to Watermark recovery from Images

10.Fabien A Petitcolas, Ross J Anderson and Markus Kuhn, Attacks on copyright

marking systems.

11.Martin Kutter and Sviatoslav Voloshynoviskiy, The Watermark Copy attack12.Niels Provos, Defending against Statistical Steganlysis

13.Barr, Bradley and Hannigan, Using Digital watermarks to mitigate the threat of copy

attacks.

14.Karen Su, Deepa Kundur and Dmitrios Hatzinakoa, A novel approach to collusion

resistant Video watermarking.

15.Stefan Katzenbeiser and Helmut Beith, Securing symmetric watermarking schemes

against protocol attacks.

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