Toolmark variability and quality depending on the fundamental parameters: Angle of attack, toolmark depth and substrate material

Baiker, Martin; Pieterman, René; Zoon, Peter

doi:10.1016/j.forsciint.2015.03.003

Cited by 19 publications

(19 citation statements)

References 6 publications

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“…The term 'toolmark' is a well established concept in the discipline of forensic science. For example, it is not uncommon for tools such as screwdrivers to be used during the undertaking of a physical crime when obtaining entry to a prohibited area (Baiker et al 2015). A screwdriver may be used to pry open a door, and due to the contact between the tool and the surface, the tool will leave specific characteristic impressions and marks behind.…”

Section: Toolmarksmentioning

confidence: 99%

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

Horsman

Errickson

2019

Science & Justice

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There are an abundance of measures available to the standard digital device users which provide the opportunity to act in an anti-forensic manner and conceal any potential digital evidence denoting a criminal act. Whilst there is a lack of empirical evidence which evaluates the scale of this threat to digital forensic investigations leaving the true extent of engagement with such tools unknown, arguably the field should take proactive steps to examine and record the capabilities of these measures. Whilst forensic science has long accepted the concept of toolmark analysis as part of criminal investigations, 'digital tool marks' (DTMs) are a notion rarely acknowledged and considered in digital investigations. DTMs are the traces left behind by a tool or process on a suspect system which can help to determine what malicious behaviour has occurred on a device. This article discusses and champions the need for DTM research in digital forensics highlighting the benefits of doing so.

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Section: Toolmarksmentioning

confidence: 99%

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

Horsman

Errickson

2019

Science & Justice

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“…After preprocessing, similarity scores are commonly computed using the Cross-Correlation (CC) as proposed by the National Institute of Standards and Technology (NIST) for comparing ballistic toolmarks [10,11]. This approach is either applied globally on the whole profile [4,5,6] or locally [8]. Another similarity measure based on locally normalized squared distances, the so-called relative distance, is propose by Bachrach et al [2].…”

Section: Introductionmentioning

confidence: 99%

“…The common challenge for comparing striated toolmarks lies in distinguishing individual characteristics of the tool while being invariant to class and sub-class characteristics [4]. Further, parameters like angle of attack (from now on referred to as α), substrate material and axial rotation have a major impact on toolmarks [5]. Baiker et al [4] showed that when comparing toolmarks with different α, for differences of 30 • the error rate is more than a magnitude higher than for differences of 15 • , i.e.…”

Section: Introductionmentioning

confidence: 99%

Learning a Similarity Measure for Striated Toolmarks using Convolutional Neural Networks

Keglevic

Sablatnig

2016

7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)

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We propose TripNet a method for calculating similarities between striated toolmarks. The objective for this system is to distinguish the individual characteristics of tools while being invariant to class and sub-class characteristics, and varying parameters like angle of attack. Instead of designing a handcrafted feature extractor customized for this task we propose the use of a Convolutional Neural Network (CNN). With the proposed system 1D profiles extracted from images of striated toolmarks are mapped into an embedding. The system is trained by minimizing a triplet loss function so that a similarity measure is defined by the L 2 distance in this embedding. The performance is evaluated on the NFI Toolmark database containing 300 striated toolmarks of screwdrivers published by the National Forensic Institute of the Netherlands. The proposed system is able to adapt to a large range of angles of attack between 15 • and 75 • , achieving a Mean Average Precision (MAP) of 0.95 for toolmark comparisons with differences in angle of attack of 15 • to 45 • ; for differences of 15 • to 60 • a MAP of 0.78 is achieved.

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“… studied the effects of the screwdriver shaft rotation angle on the formed marks and indicated that similarity and repeatability among the marks decrease with increasing rotation angle (0–75°). In a similar manner, the influence of the attack angle, substrate material, and mark depth on the similarity between the tool‐marks and their simulation were discussed . Petraco et al.…”

mentioning

confidence: 97%

“…Garcia et al (9) studied the effects of the screwdriver shaft rotation angle on the formed marks and indicated that similarity and repeatability among the marks decrease with increasing rotation angle (0-75°). In a similar manner, the influence of the attack angle, substrate material, and mark depth on the similarity between the tool-marks and their simulation were discussed (10,11). Petraco et al (12) used nine screwdrivers to generate replicable sets of ideal striation patterns; the authors scanned the striation mark images along a certain direction and coded an obtained gray image into a highdimensional feature vector.…”

mentioning

confidence: 99%

Quantitative Statistics and Identification of Tool‐Marks

Yang

et al. 2019

Journal of Forensic Sciences

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This study was designed to establish a feature identification method of tool‐mark 2D data. A uniform local binary pattern histogram operator was developed to extract the tool‐mark features, and the random forest algorithm was adopted to identify these. The presented method was used to conduct five groups of experiments with a 2D dataset of known matched and nonmatched tool‐marks made by bolt clippers, cutting pliers, and screwdrivers. The experimental results show that the proposed method achieved a high rate of identification of the tool‐mark samples generated under identical conditions. The proposed method effectively overcomes the disadvantage of unstable illumination of 2D tool‐mark image data and avoids the difficulty in mark inspection caused by manually preset parameters in the existing methods, thus reducing the uncertainty of inspected results.

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Toolmark variability and quality depending on the fundamental parameters: Angle of attack, toolmark depth and substrate material

Cited by 19 publications

References 6 publications

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

When finding nothing may be evidence of something: Anti-forensics and digital tool marks

Learning a Similarity Measure for Striated Toolmarks using Convolutional Neural Networks

Quantitative Statistics and Identification of Tool‐Marks

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