JPEG compression is one of the major image compression methods and is widely used on the Internet. In addition, identifying traces of JPEG compression and double JPEG compression (DJPEG) is crucial in the image forensics field. Therefore, JPEG compression detection and DJPEG compression detection are two of the popular image authentication methods. Many feature-based JPEG detection methods have been proposed for that purpose, and there have been outstanding improvements in DJPEG detection with the development of deep learning. A number of anti-forensics of JPEG detection that counter featurebased detectors have been proposed but only a few techniques that counter DJPEG have been researched. This paper explores whether JPEG reconstruction methods, including restoration and anti-forensics of JPEG detection, can deceive JPEG and DJPEG detectors. We demonstrate that existing anti-forensics of JPEG detection can deceive both JPEG and DJPEG detectors well but perform poorly in non-aligned cases and degrade the image quality. We propose a convolutional neural network (CNN) based anti-forensics method to improve the performance of anti-forensics so that they can proficiently deceive JPEG and DJPEG detectors with higher image quality. Moreover, we explore the generalization algorithm to handle the real scenario.

Objective Several lines of evidence indicate verbal abuse (VA) critically impacts the developing brain; however, whether VA results in changes in brain neurochemistry has not been established. Here, we hypothesized that exposure to recurrent parental VA elicits heightened glutamate (Glu) responses during the presentation of swear words, which can be measured with functional magnetic resonance spectroscopy (fMRS). Methods During an emotional Stroop task consisting of blocks of color and swear words, metabolite concentration changes were measured in the ventromedial prefrontal cortex (vmPFC) and the left amygdalohippocampal region (AMHC) of healthy adults (14 F/27 M, 23 ± 4 years old) using fMRS. The dynamic changes in Glu and their associations with the emotional state of the participants were finally evaluated based on 36 datasets from the vmPFC and 30 from the AMHC. Results A repeated-measures analysis of covariance revealed a modest effect of parental VA severity on Glu changes in the vmPFC. The total score on the Verbal Abuse Questionnaire by parents (pVAQ) was associated with the Glu response to swear words (D Glu Swe ). The interaction term of D Glu Swe and baseline N-acetyl aspartate (NAA) level in the vmPFC could be used to predict state-trait anxiety level and depressive mood. We could not find any significant associations between D Glu Swe in the AMHC and either pVAQ or emotional states. Conclusion Parental VA exposure in individuals is associated with a greater Glu response towards VA-related stimuli in the vmPFC and that the accompanying low NAA level may be associated with anxiety level or depressive mood.