Probabilistic or bias-based fingerprinting codes to counter collusion attacks are applied to enhance the security of transaction watermarking applications. Into every media copy to be distributed is embedded a unique fingerprint via watermarking techniques. With it a distributor is able to trace back unauthorized redistributed versions to its source with a high probability even if the media was subject to a collusion attack. The seminal Tardos fingerprinting codes and all its derivatives rely on position independent fingerprints. Hence in most work it is assumed that the attackers also rely on the position independence when creating the media forgery containing a manipulated fingerprint. However, they need not follow this assumption. In this work we present a novel iterative attack model that does not rely on the position independence but could be applied in practice. The corresponding attacks iteratively adapt the manipulated fingerprint with the intention to maximally reduce their accusation scores in order to escape the accusation. For practical collusion sizes, the attacks show better performance than other attacks typically discussed in literature. In other words, the attacks result in manipulated fingerprints that lead to higher error rates of the fingerprinting scheme, compared to the attacks discussed in literature.