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The prevalence of voice spoofing attacks in today’s digital world has become a critical security concern. Attackers employ various techniques, such as voice conversion (VC) and text-to-speech (TTS), to generate synthetic speech that imitates the victim’s voice and gain access to sensitive information. The recent advances in synthetic speech generation pose a significant threat to modern security systems, while traditional voice authentication methods are incapable of detecting them effectively. To address this issue, a novel solution for logical access (LA)-based synthetic speech detection is proposed in this paper. SpoTNet is an attention-based spoofing transformer network that includes crafted front-end spoofing features and deep attentive features retrieved using the developed logical spoofing transformer encoder (LSTE). The derived attentive features were then processed by the proposed multi-layer spoofing classifier to classify speech samples as bona fide or synthetic. In synthetic speeches produced by the TTS algorithm, the spectral characteristics of the synthetic speech are altered to match the target speaker’s formant frequencies, while in VC attacks, the temporal alignment of the speech segments is manipulated to preserve the target speaker’s prosodic features. By highlighting these observations, this paper targets the prosodic and phonetic-based crafted features, i.e., the Mel-spectrogram, spectral contrast, and spectral envelope, presenting an effective preprocessing pipeline proven to be effective in synthetic speech detection. The proposed solution achieved state-of-the-art performance against eight recent feature fusion methods with lower EER of 0.95% on the ASVspoof-LA dataset, demonstrating its potential to advance the field of speaker identification and improve speaker recognition systems. 

With the advent of automated speaker verifcation (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofng attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current oferings in this arena fall short of a unifed and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classifed, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the efectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofng detection using hand-crafted features, deep learning, and end-to-end spoofng countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unifed solutions to voice spoofng evaluation and speaker verifcation, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofng counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing). 

Abstract In cultivated tetraploid potato (Solanum tuberosum), reduction to diploidy (dihaploidy) allows for hybridization to diploids and introgression breeding and may facilitate the production of inbreds. Pollination with haploid inducers yields maternal dihaploids, as well as triploid and tetraploid hybrids. Dihaploids may result from parthenogenesis, entailing the development of embryos from unfertilized eggs, or genome elimination, entailing missegregation and the loss of paternal chromosomes. A sign of genome elimination is the occasional persistence of haploid inducer DNA in some dihaploids. We characterized the genomes of 919 putative dihaploids and 134 hybrids produced by pollinating tetraploid clones with three haploid inducers: IVP35, IVP101, and PL-4. Whole-chromosome or segmental aneuploidy was observed in 76 dihaploids, with karyotypes ranging from 2n=2x-1=23 to 2n=2x+3=27. Of the additional chromosomes in 74 aneuploids, 66 were from the non-inducer parent and 8 from the inducer parent. Overall, we detected full or partial chromosomes from the haploid inducer parent in 0.87% of the dihaploids, irrespective of parental genotypes. Chromosomal breaks commonly affected the paternal genome in the dihaploid and tetraploid progeny, but not in the triploid progeny, correlating instability to sperm ploidy and to haploid induction. The residual haploid inducer DNA discovered in the progeny is consistent with genome elimination as the mechanism of haploid induction. 

Domestication of the apple was mainly driven by interspecific hybridization. In the present study, we report the haplotype-resolved genomes of the cultivated apple (Malus domesticacv. Gala) and its two major wild progenitors,M. sieversiiandM. sylvestris. Substantial variations are identified between the two haplotypes of each genome. Inference of genome ancestry identifies ~23% of the Gala genome as of hybrid origin. Deep sequencing of 91 accessions identifies selective sweeps in cultivated apples that originated from either of the two progenitors and are associated with important domestication traits. Construction and analyses of apple pan-genomes uncover thousands of new genes, with hundreds of them being selected from one of the progenitors and largely fixed in cultivated apples, revealing that introgression of new genes/alleles is a hallmark of apple domestication through hybridization. Finally, transcriptome profiles of Gala fruits at 13 developmental stages unravel ~19% of genes displaying allele-specific expression, including many associated with fruit quality.