With unprecedented advances in genetic engineering we are starting to see progressively more original examples of synthetic life. As such organisms become more common it is desirable to be able to distinguish between natural and artificial life forms. In this paper, we present this challenge as a generalized version of Darwin's original problem, which he so brilliantly addressed in On the Origin of Species. After formalizing the problem of determining origin of samples we demonstrate that the problem is in fact unsolvable, in the general case, if computational resources of considered originator algorithms have not been limited and priors for such algorithms are known to be equal. Our results should be of interest to astrobiologists and scientists interested in producing a more complete theory of life, as well as to AI-Safety researchers.

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On the Origin of Samples: Attribution of Output to a Particular Algorithm.

The Hard Problem of consciousness has been dismissed as an illusion. By showing that computers are capable of experiencing, we show that they are at least rudimentarily conscious with potential to eventually reach superconsciousness. The main contribution of the paper is a test for confirming certain subjective experiences in a tested agent. We follow with analysis of benefits and problems with conscious machines and implications of such capability on future of computing, machine rights and artificial intelligence safety.

Detecting Qualia in Natural and Artificial Agents.

Most of no-reference image quality assessment (NR-IQA) techniques reported in literature have utilized transform coefficients, which are modeled using curve fitting to extract features based on natural scene statistics (NSS). The performance of NR-IQA techniques that utilize curve-fitting suffers from degradation in performance because the distribution of curve fitted NSS features deviate from the statistical distribution of a distorted image. Although deep convolutional neural networks (DCNNs) have been used for NR-IQA that are NSS model-independent but their performance is dependent upon the size of training data. The available datasets for NR-IQA are small, therefore data augmentation is used that affects the performance of DCNN based NR-IQA techniques and is also computationally expensive. This work proposes a new patch-based NR-IQA technique, which utilizes features extracted from discrete cosine transform coefficients. The proposed technique is curve fitting independent and helps in avoiding errors in the statistical distribution of NSS features. It relies on global statistics to estimate image quality based on local patches, which allow us to decompose the statistics of images. The proposed technique divides the image into patches and extracts nine handcrafted features i.e., entropy, mean, variance, skewness, kurtosis, mobility, band power, energy, complexity, and peak to peak value. The extracted features are used with a support vector regression model to predict the image quality score. The experimental results have shown that the proposed technique is database and image content-independent. It shows better performance over a majority of distortion types and on images taken in real-time.

Natural scene statistics model independent no-reference image quality assessment using patch based discrete cosine transform

A secret sharing scheme in halftone images is proposed in this research. Given a secret halftone image, several gray-level images with the same resolution will be chosen from the database to collaboratively carry the secret information. The selected images will be transferred to halftone ones using Multi-scale Error Diffusion (MED), with the constraint imposed by the pixels of halftone secret image. The modified MED ensures that the resultant pixels of host halftone images should satisfy the required conditions such that collecting all the processed halftone images can successfully reveal the secret one. In addition to facilitating the perfect extraction of hidden information, maintaining the quality of all the halftone images in this secret sharing scenario is the other major objective, which is achieved by the usage of MED and selection of suitable host images. The experimental results demonstrate the satisfactory performance of the proposed method.

Visual secret sharing in halftone images by multi-scale error diffusion

The existing approaches of block based recovery of progressive secret sharing paradigm have support for limited $${k}$$
 value in (k, n). Sian et al.’s as well Quan et al.’s block based visual secret sharing scheme has limited bound; works for a limited $${k}$$
 value of $$\left( {{k},{~n}} \right)$$
 threshold scheme. A novel threshold based (any value of $${k~and~~n}$$
 ) block recovery in progressive secret sharing has been introduced and analyzed in this paper. Scheme achieves higher value of threshold than the schemes proposed by Sian et al.’s as well Quan et al.’s. The scheme discusses the sharing ability in progressive block based secret sharing up to any general number of thresholds. The contrast of the recovered block wise secret is reasonably good and the scheme allows for perfect security using at least $${k}$$
 numbers of participants before revealing any of the blocks of secret image. The work in this paper offering a reference point for developing solutions needed in the area of secure image sharing techniques.

A Novel Security Technique Based on Controlled Pixel Based Encryption of Image Blocks for Sharing a Secret Image

ESSVCS: An Enriched Secret Sharing Visual Cryptography.- Extended Visual Cryptography Scheme for Multiple-Secrets Continuous-Tone Images.- Ideal Secure Multi-Secret Visual Cryptography Scheme with Ring Shares.- Password Protected Visual Cryptography via Cellular Automaton Rule 30.- A Novel Perceptual Secret Sharing Scheme.- Visual Cryptography Based Watermarking.- Flexible Visual Cryptography Scheme and Its Application.

Feng Liu

Papers

Transactions on data hiding and multimedia security IX : special issue on visual cryptography