## 3 Selective Redundancy Removal
### Removing redundancies  
vs. _modifying data_  
As the objective of steganography is concealing messages within apparently innocent carrier objects, efforts have focused along two main directions: increasing the capacity (the quantity of covert data that can be embedded into a cover file), and decreasing the detectability, _i.e.,_ reducing the reliability of detection schemes. With the hiding techniques proposed so far, only a fraction of data can carry hidden messages without easy detection. It follows that the larger the substrate, the higher is the quantity of information which can be covertly transported. Natively large substrates such as image, audio and video files are then the ideal candidates to achieve high capacities.  
On the other hand, large files need to be stored and transferred somehow, exactly as their smaller companions, requiring compression techniques to reduce data size and save on storage and bandwidth. The key principle upon which (lossless) compression lies is finding and removing statistical redundancies. Compressed files are, at least to a certain extent, redundancy-free. Having a reduced set of redundancies to manipulate, and because no value is given to the original support, which is only used as a decoy to conceal the information exchange, traditional steganographic methods have concentrated on data alteration. The most studied hiding technique, Least Significant Bits (LSB) substitution, for example, embeds secret data into an image by overwriting parts of the LSB plane. Even LSB matching, which avoids structural asymmetry [8] and is therefore not vulnerable to attacks devised for LSB substitution, essentially replaces pixel values.  
If smaller substrates, especially text files, are under consideration, the requirement for compression is much less crucial, because of the diminished impact on storage and transmission resources. Consequently, information irrelevant to meaning is allowed, and frequently found, in such substrates. If not all of those redundancies are eliminated, but only a subset thereof, then a message can be embedded. This is the basic idea behind SRR. Note that even text files may become too big and involved to be easily manageable, and therefore file size or untidiness is never completely of no concern. The application of any method whatsoever that reduces file size, or simplifies processingactivities such as rendering, can thus be justifiable and cannot automatically raise suspects of a concealed information transfer.  
A substrate, and the process that generates it, constitute the environment defining a SRR instance. In the following, when referencing specific environments in which SRR can be applied, the process will be omitted for simplicity if it can be derived from the context.  
SRR, although easily understood and applicable in the context of small files such as HTML documents, is not limited to those substrates. In fact, selective removal of redundant information can be applied to any substrate. For example, the technique can be utilized on multimedia data, by engineering modified versions of the standard compression algorithms, able to leave some amount of redundancy untouched, and masquerade that as the effect of some tradeoff between compression rate and speed. This is an open subject for future research, and this author plans to investigate the issue further.