Adaptive filters generally consist of two distinct parts:

a filter, whose structure is designed to perform a desired pro­cessing function, and an adaptive algorithm for adjusting the parameters (coefficients) of that filter. The many possible combi­nations of filter structures and the adaptive laws /governing them/ lead to a sometimes bewildering variety of adaptive filters.

We focus on what is, perhaps, the simplest class of filter structure: linear filters with a finite impulse response (FIR). Note that the filter output is a linear combination of a finite number of past inputs. The filter is not recursive (i.e., conta­ins no feedback). This property leads to particularly simple adap­tive algorithms.

Having specified the filter structure it is next required to design an adaptive algorithm for adjusting its coefficients. We are to consider adaptive laws whose objective is to minimize the energy of the filter output (i.e., the output variance or the output sum of squares). The need to minimize this particular cost function arises in many applications involving least-squares es­timation, such as adaptive noise canceling, adaptive line enhan­cement, and adaptive spectral estimation.

We are to present two adaptive algorithms for FIR fillers: the recursive least-squares (RLS) algorithm and the Widrow-Hoff least-mean-squares (LMS) algorithm. The LMS algorithm has gained considerable popularity since the early 1960s. Its simplicity makes it attractive for many applications in which computational requirements need to be minimized. The RLS algorithm has been used extensively for system identification and time-series analysis. In spite of its potentially superior performance, its use in signal processing applications has been relatively limited, due to its higher computational requirements. In recent years there has been renewed interest in the RLS algorithm, especially in its "fast" (computationally efficient) versions. The RLS algorithm has been applied to adaptive channel equalization adaptive array processing and other problems.

The concept of adaptation in digital filtering has proven to be a powerful and versatile means of signal proces­sing in applications where precise a priori filter design is im­practical. For the most part, such signal processing applications have relied on the well-known adaptive finite impulse response (FIR) filter configuration. Yet, in practice, situations commonly arise wherein the nonrecursive nature of this adaptive filter results in a heavy computational load. Consequently, in recent years acti­ve research has attempted to extend the adaptive FIR filter into the more general feedback or infinite impulse response (IIR) con­figuration. The immediate reward lies in the substantial decrease in computation that a feedback filter can offer over an FIR filter. This computational improvement comes at certain costs, however. In particular, the presence of feedback makes filter stability an issue and can impact adversely on the algorithm's convergence time and the general numerical sensitivity of the filter. Even so, the largest obstacle to the wide use of adaptive IIR filters is the lack of robust and well-understood algorithms, for adjusting the required filter gains. The classes of algorithms to be currently under development are to be explored those based on minimum mean-square-error concepts, and another which has its roots in nonlinear stability theory. The basic derivation of each will be pre­sented and certain aspects of performance examined. Other key de­sign concerns, such as the fact that certain algorithms require the use of specific filter structures, will also be to be illumi­nated.

Text 4. Digital Steganography Technology

I.Steganography, which comes from the Greek words "steganos," or "covered" and "graphy," or "writing," can be used to establish covert channels between an insider and one, or more, external entities. Essentially, steganography is used to "cover" the "writing" so as to conceal its very existence. Modern use is called digital steganography.

In April 2006, the National Science and Technology Council released the Federal Plan for Cyber Security and Information Assurance Research and Development, which defines steganography as "the art and science of writing hidden messages in such a way that no one apart from the intended recipient knows of the existence of the message."

Simple steganographic techniques have been in use for hundreds of years, but with the increasing use of files in an electronic format new techniques for information hiding have become possible.

Figure 1 shows how information hiding can be broken down into different areas. Steganography can be used to hide a message intended for later retrieval by a specific individual or group. In this case the aim is to prevent the message being detected by any other party.

The other major area of steganography is copyright marking, where the message to be inserted is used to assert copyright over a document. This can be further divided into watermarking and fingerprinting which will be discussed later.

Steganography and encryption are both used to ensure data confidentiality. However the main difference between them is that with encryption anybody can see that both parties are communicating in secret. Steganography hides the existence of a secret message and in the best case nobody can see that both parties are communicating in secret. This makes steganography suitable for some tasks for which encryption isn’t, such as copyright marking. Adding encrypted copyright information to a file could be easy to remove but embedding it within the contents of the file itself can prevent it being easily identified and removed.

Figure 2 shows a comparison of different techniques for communicating in secret. Encryption allows secure communication requiring a key to read the information. An attacker cannot remove the encryption but it is relatively easy to modify the file, making it unreadable for the intended recipient.

Digital signatures allow authorship of a document to be asserted. The signature can be removed easily but any changes made will invalidate the signature, therefore integrity is maintained.

Steganography provides a means of secret communication which cannot be removed without significantly altering the data in which it is embedded. The embedded data will be confidential unless an attacker can find a way to detect it.

II.Attacks

Information hiding techniques still suffer from several limitations leaving them open to attack and robustness criteria vary between different techniques. Attacks can be broadly categorized although some attacks will fit into multiple categories.

Basic Attacks:

Basic attacks take advantage of limitations in the design of the embedding techniques. Simple spread spectrum techniques, for example, are able to survive amplitude distortion and noise addition but are vulnerable to timing errors. Synchronisation of the chip signal is required in order for the technique to work so adjusting the synchronisation can cause the embedded data to be lost.

It is possible to alter the length of a piece of audio without changing the pitch and this can also be an effective attack on audio files.

Robustness Attacks:

Robustness attacks attempt to diminish or remove the presence of a watermark. Although most techniques can survive a variety of transformations, compression, noise addition, etc they do not cope so easily with combinations of them or with random geometric distortions. If a series of minor distortions are applied the watermark can be lost while the image remains largely unchanged. What changes have been made will likely be acceptable to pirates who do not usually require high quality copies. Since robustness attacks involve the use of common manipulations, they need not always be malicious but could just be the result of normal usage by licensed users.

Protecting against these attacks can be done by anticipating which transformations pirates are likely to use. Embedding multiple copies of the mark using inverse transformations can increase the resistance to these attacks. However, trying to guess potential attacks is not ideal. The use of benchmarking for evaluating techniques could help to determine how robust the technique is. StirMark is a tool which applies minor geometric distortions, followed by a random frequency deviation based around the centre of the image and finally a transfer function to introduce error into all sample values similar to the effects of a scanner. StirMark can serve as a benchmark for image watermarking.

The echo hiding technique encodes zeros and ones by adding echo signals distinguished by different values for their delay and amplitude to an audio signal. Decoding can be done by detecting the initial delay using the auto-correlation of the cepstrum of the encoded signal but this technique can also be used as an attack.

If the echo can be detected then it can be removed by inverting the formula used to add it. The difficult part is detecting the echo without any knowledge of the original or the echo parameters. This problem is known as ‘blind echo cancellation’. Finding the echo can be done using a technique called cepstrum analysis.

Other attacks will attempt to identify the watermark and then remove it. This technique is particularly applicable if the marking process leaves clues that help the attacker gain information about the mark. For example an image with a low number of colours, such as a cartoon image, will have sharp peaks in the colour histogram. Some marking algorithms split these and the twin peaks attack takes advantage of this to identify the marks which can then be removed.

Presentation Attacks:

Presentation attacks modify the content of the file in order to prevent the detection of the watermark. The mosaic attack takes advantage of size requirements for embedding a watermark. In order for the marked file to be the same size as the original the file must have some minimum size to accommodate the mark. By splitting the marked file into small sections the mark detection can be confused. Many web browsers will draw images together with no visible split enabling the full image to be effectively restored while hiding the mark. If the minimum size for embedding the mark is small enough the mosaic attack is not practical. This attack can defeat web crawlers which download pictures from the Internet and check them for the presence of a client’s watermark.

Interpretation Attacks:

Interpretation attacks involve finding a situation in which the assertion of ownership is prevented. Robustness is usually used to refer to the ability of the mark to survive transformations and not resistance to an algorithmic attack. Therefore the definition of robustness may not be sufficient.

One interpretation attack takes advantage of mark detection being unable to tell which mark came first if multiple marks are found. If the owner publishes a document, d + w (where d is the original and w is the watermark) a pirate can add a second watermark w’ and claim that the document is his and that the original was d + w - w’. Though it is clear that at least one party has a counterfeit copy, it is not clear which one. This would seem to suggest the need to use other techniques to identify the original owner of a file.

Implementation Attacks:

As with other areas in computer security the implementation of a marking system can provide more opportunities for attack than the marking technique itself. If the mark detection software is vulnerable it may be possible for attackers to deceive it.

Digimarc, one of the most widely used picture marking schemes was attacked using a weakness in the implementation. Users register an ID and password with the marking service. A debugger was used to break into the software which checks these passwords and disable the checking. The attacker can change the ID and this will change the mark of already marked images. The debugger also allowed bypassing of checks to see if a mark already existed and therefore allowed marks to be overwritten.

There is a general attack on mark readers which explores an image on the boundary between no mark having been found and one being detected. An acceptable copy of the image can be iteratively generated which does not include the mark.

Clearly the software used to implement steganographic techniques needs to be secure and ideas from other areas of computer security can be used to ensure this.

Text 5. Radio Frequency Identification (RFID)

I.Radio Frequency Identification (RFID) is a type of automatic identification system. The purpose of an RFID system is to enable data to be transmitted by a portable device, called a tag, which is read by an RFID reader and processed according to the needs of a particular application. The data transmitted by the tag may provide identification or location information or specifics about the product tagged, such as price, color, date of purchase, etc. The use of RFID in tracking applications first appeared during the 1980s even though RFID was developed by allied forces in WWII so radar operators could distinguish between friendly and enemy aircraft. A basic RFID system consists of three components: an antenna or coil, a transceiver (with decoder), a transponder (RF tag) electronically programmed with unique information. When an RFID tag passes through the electromagnetic zone, it detects the reader’s activation signal. The reader decodes the data encoded in the tag’s integrated circuit (silicon chip) and the data is passed to the host computer for processing. Principal areas of application for RFID that can be currently identified include: transportation and logistics, manufacturing and processing, security.