ABSTRACT:

Forgeries of coins can either be contemporary or modern. Already in the Middle Ages, it was well known that bracteates were considerably more difficult to counterfeit than two-faced coins. The main reason is that bracteates are struck with a more complicated technology originating from goldsmithing. Therefore, most bracteate forgeries have been produced since the eighteenth century. Compared to original bracteates, modern bracteate forgeries often have the following characteristics: 1) an incorrect weight; 2) a lower relief; 3) sharper contours on the reverse; 4) an artistically clumsy design; 5) evidence of being struck with the same die if there are several specimens; and/or 6) empty fields in the background.

In this article Author discusses the problem of false interpretations of the earliest history of the Slavic people undertaken by Polish 19th century historians. He also analyses the attitude of various historians towards their colleagues' forgeries.

The copy-move forgery detection (CMFD) begins with the preprocessing until the image is ready to process. Then, the image features are extracted using a feature-transform-based extraction called the scale-invariant feature transform (SIFT). The last step is features matching using Generalized 2 Nearest- Neighbor (G2NN) method with threshold values variation. The problem is what is the optimal threshold value and number of keypoints so that copy-move detection has the highest accuracy. The optimal threshold value and number of keypoints had determined so that the detection has the highest accuracy. The research was carried out on images without noise and with Gaussian noise.

Digital speech copyright protection and forgery identification are the prevalent issues in our advancing digital world. In speech forgery, voiced part of the speech signal is copied and pasted to a specific location which alters the meaning of the speech signal. Watermarking can be used to safe guard the copyrights of the owner. To detect copy-move forgeries a transform domain watermarking method is proposed. In the proposed method, watermarking is achieved through Discrete Cosine Transform (DCT) and Quantization Index Modulation (QIM) rule. Hash bits are also inserted in watermarked voice segments to detect Copy-Move Forgery (CMF) in speech signals. Proposed method is evaluated on two databases and achieved good imperceptibility. It exhibits robustness in detecting the watermark and forgeries against signal processing attacks such as resample, low-pass filtering, jittering, compression and cropping. The proposed work contributes for forensics analysis in speech signals. This proposed work also compared with the some of the state-of-art methods.

The paper is a case study investigating the nominal and adjectival morphology in the English text of bounds to S 179, a post-Conquest forgery. The aim of the study is to determine what linguistic means of authentication were applied by an eleventh- century forger who devised a text which was supposed to look 200 years old at the time of its production, as well as to search for modern features which give the forgery away, at the same time allowing an insight into early Middle English. The study represents research into “transitional”, post-Conquest English (Faulkner 2012) and the status of English under the Norman rule.