Instagram Mobile Application Digital Forensics

In this research,we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application,Instagram.This plugin extracts personal details from Instagram users,e.g.,name,user name,mobile number,ID,direct text or audio,video,and picture messages exchanged between different Instagram users.While developing the plugin,we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work,has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed,and how the traces,as well as the URL links of visual messages,can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file,which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.

Analysis of γ-Hydroxybutyrate in Human Urine by LC-MSIMS Method and Its Forensic Application

GHB （γ-hydroxybutyrate） is becoming popular recreational drugs. As a result of its strong sedative and amnesiac effects, GHB has been implicated in a number of DFSA cases. The natural presence of GHB in the human body and its rapid elimination after ingestion make it difficult to detect and to evaluate its roles in suspected GHB-facilitated assaults. The paper describes an analytical method for the determination of GHB in urine using LC-MS/MS. Samples were acidified by ammonium chloride solution and extracted with ethyl acetate, and then the extracts were analysed by LC-MS/MS. The limit of detection was 0.05 p.g/mL （S/N = 3）. The intra- and inter-day precision was within 10.0% at three concentrations. The methods were found to be sensitive, accurate, rapid and suitable for the forensic toxicology to test GHB in real cases.

Forensic application of comet assay:an emerging technique

Postmortem interval(PMI)estimation is a recurring problem in the field of forensic medicine.Conventional methods are effective but are insufficient to estimate accurate and precise time of death or PMI.In addition,degradation of biological samples is another major problem in forensic science which affects the investigation process and misleads the result.Some previous studies reported that DNA fragmentation has strong correlation with PMI.DNA fragmentation increased with prolonged PMI.Comet assay is a rapid sensitive,versatile,reliable and cost effective technique that is specifically used for qualitative and quantitative estimation of nuclear DNA fragmentation.Due to this attribute,comet assay can help to estimate accurate and precise time of death for some extent that is for early PMI estimation.In addition,two confounding factors are responsible for DNA fragmentation:(1)micro-organism;(2)environmental condition.Here,comet assay plays a dual role:(1)partially degraded samples get repaired using repair enzyme;(2)accurate time since deposition can be measured without using repair enzyme.Furthermore,this assay can also help to identify potential exposures of environmental-released chemicals/toxicants and its deleterious effects on human population.In this way,comet assay shows its versatile applications that could be useful for forensic investigation.Therefore,with the help of this review,an attempt was made to explore the versatility of comet assay technique for forensic applications and its future perspective.

Flux blended synthesis of novel Y2O3：Eu^3＋ sensing arrays for highly sensitive dual mode detection of LFPs on versatile surfaces

In the present communication,various fluxes blended Y2 O3：Eu3＋（5 mol%） nanopowders（NPs） were successfully fabricated by solution combustion method.PXRD pattern confirms body-centered cubic structure of the prepared samples.Energy band gap（Eg） of the fabricated products was estimated and is found to be in the range of 3.13-3.32 eV.Photoluminescence（PL） emission spectra exhibit sharp and intense peaks at ～579,592,614,657,704 nm corresponding to 5 D0→7 FJ（J = 0,1,2,3 and 4） transitions of Eu3＋ ions.Significance of fluxes for enhancing the PL emissions was extensively studied.Photometric studies of the prepared samples are located in pure red region.Optimized NPs were explored as a novel sensing agent for visualization of latent fingerprints（LFPs） on various surfaces including porous,semiporous and non-porous surfaces followed by powder dusting technique.Various experiments including aging,temperature,scratching and aquatic fresh water treatment tests were performed to evaluate applicability of the fabricated NPs.Visualized LFPs exhibit well defined ridge details including most authenticated sweat pores are also revealed with high sensitivity,selectivity,little background hindrance and less toxicity.Aforementioned results evidence that the method and fabricated NPs can be considered to be simple,rapid and economical and provide novel sensing platform for LFPs visualization in prospective forensic applications.

Identification of various food residuals on denim based on hyperspectral imaging system and combination optimal strategy

As the science and technology develop,crime methods and scenes have become increasingly complex and diverse.Trace evidence analysis has become amore and more important criminal investigation technology and liquid is the main form of trace evidence.Food can provide not only energy,but clues to solve crimes.In this study,we build a hyperspectral imaging system to detect liquid residue traces,including apple juice,coffee,cola,milk and tea,on denims with light,middle and dark colors.The obtained hyperspectral images are first subjected to spectral calibration and hyperspectral data pretreatment.Subsequently,Partial Least Squares(PLS)is applied to select the informative wavelengths from the preprocessed spectra.For modeling phase,the combination optimal strategy,support vector machine(SVM)combined with random forest(RF),is developed to establish classification models.The experimental results demonstrate that the combination optimal model can achieve TPR,FPR,Precision,Recall,F1,and AUC of 83.5%,2.30%,79.7%,83.5%,81.6%,and 94.7%for classifying fabrics contaminated by various food residuals.With respect to the classification of liquid and fabric types,the combination optimalmodel also yields satisfactory classification performance.In future work,wewill expand the types of liquid,and make appropriate adjustment to algorithms for improving the robustness of classification models.This research may play a positive role in the construction of a harmonious society.